masters thesis machine learning

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Top 10 Research and Thesis Topics for ML Projects in 2022

This article features the top 10 research and thesis topics for ML projects for students to try in 2022

Text mining and text classification, image-based applications, machine vision, optimization, voice classification, sentiment analysis, recommendation framework project, mall customers’ project, object detection with deep learning.

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Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A  non-exhaustive list of open topics is listed below.

If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential topics.

Robustness of Large Language Models

Type: Master's Thesis

Prerequisites:

• Strong knowledge in machine learning
• Very good coding skills
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)
• Knowledge about NLP and LLMs

Description:

The success of Large Language Models (LLMs) has precipitated their deployment across a diverse range of applications. With the integration of plugins enhancing their capabilities, it becomes imperative to ensure that the governing rules of these LLMs are foolproof and immune to circumvention. Recent studies have exposed significant vulnerabilities inherent to these models, underlining an urgent need for more rigorous research to fortify their resilience and reliability. A focus in this work will be the understanding of the working mechanisms of these attacks.

We are currently seeking students for the upcoming Summer Semester of 2024, so we welcome prompt applications. This project is in collaboration with  Google Research .

Contact: Tom Wollschläger

References:

• Universal and Transferable Adversarial Attacks on Aligned Language Models
• Attacking Large Language Models with Projected Gradient Descent
• Representation Engineering: A Top-Down Approach to AI Transparency
• Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks

Generative Models for Drug Discovery

Type:  Mater Thesis / Guided Research

• Strong machine learning knowledge
• Proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
• Knowledge of graph neural networks (e.g. GCN, MPNN)
• No formal education in chemistry, physics or biology needed!

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain which has experienced great attention through the success of generative models. These models promise a more efficient exploration of the vast chemical space and generation of novel compounds with specific properties by leveraging their learned representations, potentially leading to the discovery of molecules with unique properties that would otherwise go undiscovered. Our topics lie at the intersection of generative models like diffusion/flow matching models and graph representation learning, e.g., graph neural networks. The focus of our projects can be model development with an emphasis on downstream tasks ( e.g., diffusion guidance at inference time ) and a better understanding of the limitations of existing models.

Contact :  Johanna Sommer , Leon Hetzel

Equivariant Diffusion for Molecule Generation in 3D

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Structure-based Drug Design with Equivariant Diffusion Models

Data Pruning and Active Learning

Type: Interdisciplinary Project (IDP) / Hiwi / Guided Research / Master's Thesis

Data pruning and active learning are vital techniques in scaling machine learning applications efficiently. Data pruning involves the removal of redundant or irrelevant data, which enables training models with considerably less data but the same performance. Similarly, active learning describes the process of selecting the most informative data points for labeling, thus reducing annotation costs and accelerating model training. However, current methods are often computationally expensive, which makes them difficult to apply in practice. Our objective is to scale active learning and data pruning methods to large datasets using an extrapolation-based approach.

Contact: Sebastian Schmidt , Tom Wollschläger , Leo Schwinn

• Large-scale Dataset Pruning with Dynamic Uncertainty

Efficient Machine Learning: Pruning, Quantization, Distillation, and More - DAML x Pruna AI

Type: Master's Thesis / Guided Research / Hiwi

The efficiency of machine learning algorithms is commonly evaluated by looking at target performance, speed and memory footprint metrics. Reduce the costs associated to these metrics is of primary importance for real-world applications with limited ressources (e.g. embedded systems, real-time predictions). In this project, you will work in collaboration with the DAML research group and the Pruna AI startup on investigating solutions to improve the efficiency of machine leanring models by looking at multiple techniques like pruning, quantization, distillation, and more.

Contact: Bertrand Charpentier

• The Efficiency Misnomer
• A Gradient Flow Framework for Analyzing Network Pruning
• Distilling the Knowledge in a Neural Network
• A Survey of Quantization Methods for Efficient Neural Network Inference

Deep Generative Models

Type:  Master Thesis / Guided Research

• Strong machine learning and probability theory knowledge
• Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
• Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

• Flow Matching for Generative Modeling
• Auto-Encoding Variational Bayes
• Denoising Diffusion Probabilistic Models 
• Structured Denoising Diffusion Models in Discrete State-Spaces

Graph Structure Learning

Type:  Guided Research / Hiwi

• Optional: Knowledge of graph theory and mathematical optimization

Graph deep learning is a powerful ML concept that enables the generalisation of successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results in a vast range of applications spanning the social sciences, biomedicine, particle physics, computer vision, graphics and chemistry. One of the major limitations of most current graph neural network architectures is that they often rely on the assumption that the underlying graph is known and fixed. However, this assumption is not always true, as the graph may be noisy or partially and even completely unknown. In the case of noisy or partially available graphs, it would be useful to jointly learn an optimised graph structure and the corresponding graph representations for the downstream task. On the other hand, when the graph is completely absent, it would be useful to infer it directly from the data. This is particularly interesting in inductive settings where some of the nodes were not present at training time. Furthermore, learning a graph can become an end in itself, as the inferred structure can provide complementary insights with respect to the downstream task. In this project, we aim to investigate solutions and devise new methods to construct an optimal graph structure based on the available (unstructured) data.

Contact : Filippo Guerranti

• A Survey on Graph Structure Learning: Progress and Opportunities
• Differentiable Graph Module (DGM) for Graph Convolutional Networks
• Learning Discrete Structures for Graph Neural Networks

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

A Machine Learning Perspective on Corner Cases in Autonomous Driving Perception  

Type: Master's Thesis 

Industrial partner: BMW 

Prerequisites: 

• Strong knowledge in machine learning 
• Knowledge of Semantic Segmentation  
• Good programming skills 
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch) 

Description: 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example semantic segmentation. While the environment in datasets is controlled in real world application novel class or unknown disturbances can occur. To provide safe autonomous driving these cased must be identified. 

The objective is to explore novel class segmentation and out of distribution approaches for semantic segmentation in the context of corner cases for autonomous driving. 

Contact: Sebastian Schmidt

References: 

• Segmenting Known Objects and Unseen Unknowns without Prior Knowledge 
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos  
• Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family  
• Description of Corner Cases in Automated Driving: Goals and Challenges 

Active Learning for Multi Agent 3D Object Detection 

Type: Master's Thesis  Industrial partner: BMW 

• Knowledge in Object Detection 
• Excellent programming skills 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.   

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.  

• Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving   
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos   
• KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
• Towards Open World Active Learning for 3D Object Detection   

Graph Neural Networks

Type:  Master's thesis / Bachelor's thesis / guided research

• Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

• Semi-supervised classification with graph convolutional networks
• Relational inductive biases, deep learning, and graph networks
• Diffusion Improves Graph Learning
• Weisfeiler and leman go neural: Higher-order graph neural networks
• Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type:  Master's thesis / guided research

• Proficiency with Python and deep learning frameworks (JAX or PyTorch)
• Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
• Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

• Directional Message Passing for Molecular Graphs
• Neural message passing for quantum chemistry
• Learning to Simulate Complex Physics with Graph Network
• Ab initio solution of the many-electron Schrödinger equation with deep neural networks
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

• Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
• Strong background in mathematical optimization (preferably combined with Machine Learning setting)
• Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
• (Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

• Intriguing properties of neural networks
• Explaining and harnessing adversarial examples
• SoK: Certified Robustness for Deep Neural Networks
• Certified Adversarial Robustness via Randomized Smoothing
• Formal guarantees on the robustness of a classifier against adversarial manipulation
• Towards deep learning models resistant to adversarial attacks
• Provable defenses against adversarial examples via the convex outer adversarial polytope
• Certified defenses against adversarial examples
• Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

• Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger ,   Dominik Fuchsgruber ,   Bertrand Charpentier

• Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• Predictive Uncertainty Estimation via Prior Networks
• Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
• Evidential Deep Learning to Quantify Classification Uncertainty
• Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type:  Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

• Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Gradient-based Hierarchical Clustering
• Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

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master-thesis

Here are 87 public repositories matching this topic..., johnmartinsson / bird-species-classification.

Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.

• Updated Oct 11, 2023

Tim-HW / HW-BlueRov2-Sonar-based-SLAM

This project will evaluate simultaneous localisation and mapping (SLAM) algorithms for fusing sonar with DVL and IMU to produce maps for autonomous underwater vehicle (AUV) navigation for underwater ROV

• Updated Jan 24, 2024

meurissemax / autonomous-drone

Master's thesis about autonomous navigation of a drone in indoor environments carried out to obtain the degree of Master of Science in Computer Science Engineering (University of Liège, academic year 2020-2021).

• Updated Jul 13, 2021

dpuljic01 / financial-dashboard

Masters Thesis - Fintech Dashboard

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LasseRegin / master-thesis-deep-learning

Code for my master thesis in Deep Learning: "Generating answers to medical questions using recurrent neural networks"

• Updated Jul 16, 2017

RealityNet / McAFuse

Toolset to analyze disks encrypted with McAFee FDE technology

• Updated Mar 11, 2021

harshildarji / thesis

Master's thesis, Uni Passau

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Boren / MasterThesis

Deep Convolutional Neural Networks for Semantic Segmentation of Multi-Band Satellite Images

• Updated May 30, 2018

thomasSve / Msc_Multi_label_ZeroShot

Code for master thesis on Zero-Shot Learning in multi-label scenarios

• Updated Mar 28, 2018

kdevo / chaos-rrs

Chaos - a first of its kind framework for researching Reciprocal Recommender Systems (RRS).

• Updated Nov 7, 2021

JanPokorny / speed-climbing-mapping

Mapping videos of speed climbers onto a virtual wall using ML, OpenCV, and maths. Implementation of my master's thesis.

• Updated Jul 20, 2023

danielathome19 / Form-NN

Master thesis project - a hybrid Neural Network-Decision Tree system and dataset for classical music form recognition and analysis.

• Updated Dec 9, 2022

lukaselmer / hierarchical-paragraph-vectors

Hierarchical Paragraph Vectors

• Updated Sep 21, 2015

KyleOng / starreco

State-of-The-Art Rating-based RECOmmendation system: pytorch lightning implementation

• Updated Oct 10, 2023

lcebear / memoryDialogueBot

Master Thesis Project: A memory based dialogue agent

• Updated Dec 20, 2020

develooper1994 / MasterThesis

My Master Thesis experimentation source codes

• Updated Apr 6, 2021

jrmak / FNNR-ABM-Primate

An agent-based model (with a web simulation) for Guizhou "golden" monkey population and movements using the Mesa Python framework; thesis project + human/GTGP expansion; Summer 2018-Winter 2019

• Updated Mar 22, 2020

rand-asswad / muallef

Study of Music Information Retrieval (MIR) methods for multi-pitch estimation and onset detection.

• Updated Dec 8, 2022

EivindArvesen / master_code

Various code from my master's project

• Updated Jan 9, 2019

Josef-Djarf / sEMG-Sim

Source code for multiple parameter modelling of synthetic electromyography data.

• Updated Feb 21, 2024

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Guide to Earning a Master's in Machine Learning

Aaron Perkowitz

Contributing Writer

Learn about our editorial process .

Updated November 9, 2023

Reviewed by

Victoria Ivy Southall

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Are you ready to discover your college program?

Machine learning (ML), a subset of automated intelligence (AI), uses algorithms and data to help computers learn without direct human interference. According to LinkedIn, ML engineer was one of the fastest-growing job titles from 2017-2022.

Companies continuously invest time and resources into machine learning, which currently accounts for the highest percentage of funding in AI. The machine learning industry covers multiple sectors and has an expected annual growth rate of 39% from 2020-2030, according to Market Research Future .

A master's in machine learning program teaches individuals the fundamental principles and skills to pursue careers in ML. The following page discusses the costs, benefits, and career choices of attending a master's in ML program.

Why Get a Machine Learning Degree?

Walter Pitts and Warren McCulloch published the first paper regarding machine learning in 1943. This paper, which discussed the mathematical models of neural networks, became the groundwork for future machine learning developments.

Machine learning can be used in almost any field with large data sets. These data sets and ML then make predictions or decisions. Companies like Yelp, Google, and Facebook employ machine learning to help acquire users, customer support, and fraud detection. ML careers include roles like data scientist and robotics engineer.

The World Economic Forum estimates that 97 million new jobs will be created from advances in technology — including AI — from 2020-2025. Experts in AI and ML may be in-demand as companies and careers shift toward new technologies.

Individuals interested in creative problem-solving, modeling, and programming can thrive in the ML industry. Graduates of ML master's programs can pursue related careers and may have a competitive advantage against candidates without related degrees. ML bootcamp graduates can also compete in the job market, but bootcamps have a limited scope and timeframe compared to master's degrees.

• Career Opportunities : Experts expect AI and ML to grow rapidly over the next decade. Machine learning candidates can pursue various careers across industries. Graduates of an ML program can demonstrate their education and expertise in this field.
• Positive Impact : ML experts may work on projects with real-world benefits. Healthcare, farming, and emergency relief companies have all used AI to help people.
• Hands-On Experience : Professionals in machine learning can work directly to code and improve programs. Individuals interested in seeing their work come to fruition may enjoy careers in the field.

Popular Online Programs

Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below.

What To Expect From Master's in Machine Learning Programs

Master's in machine learning programs prepare students to pursue careers in the growing AI field. Programs focus on teaching students about ML principles and how to incorporate technology into different fields.

Individuals pursuing master's in machine learning degrees can expect approximately 30 credit hours of courses over 1-3 years. Program tuition can range from $10,000-$80,000. Institutions offer many types of ML programs, including master's degrees in ML or degrees in AI with a concentration in ML.

Enrollees explore courses in machine learning, deep learning, and other technical and AI-related topics. Some master's degrees also require a capstone project or thesis paper.

Admission Requirements

Each master's in ML program has specific admission requirements, but candidates typically submit online applications, send official transcripts, and pay the relevant fees. Each applicant must hold a bachelor's degree , usually in a subject related to computer science. Individuals with GPAs of 3.0 GPA may receive preference in certain programs.

Programs may require GRE scores, and some schools set a minimum score of 320. Master degree prerequisites may include programming courses , math courses, or an understanding of the two.

Degree and Specialization Options

Individuals interested in attending a machine learning program can choose from a master's degree in ML, a combination degree in AI and ML, or a degree in AI with a concentration in ML. An attendee's program choice depends on the branch of AI they plan to focus on.

Students must also choose their preferred learning experience: online, in-person, or hybrid. Online students may have additional flexibility and asynchronous classes, while in-person students experience additional networking opportunities.

Popular Master's in Machine Learning Courses

Machine learning programs offer different curriculums but usually include core courses and electives. While coursework varies among programs, the following list includes some of the most common classes.

• Machine Learning : This introductory course covers ML theories and principles. Topics may include model selection, overfitting, and decision theory.
• Deep Learning : Deep learning refers to a subdivision of machine learning that attempts to copy the way a human thinks. This course explains the theory and uses of deep learning.
• Design and Analysis of Algorithms : Students can learn about the complexity of data and related issues in this course. Classes cover graphing algorithms, lower bounds, and recurrences.
• Computer Vision: Computer vision allows computers to comprehend objects in images and videos. Coursework discusses real-life applications of this technology, along with visual processing, edge detection, and image modeling.

How Much Will a Master's in Machine Learning Cost?

Costs for a master's in machine learning vary significantly and may extend from $10,000-$80,000. Tuition prices depend on factors like institution prestige, out-of-state fees, and course load.

Enrollees should also budget for living expenses such as housing, food, and transportation. University students can apply for financial aid such as scholarships , fellowships, and grants to help with tuition and living costs. The links below provide additional information on how to pay for a degree .

Jobs for Graduates With Machine Learning Degrees

Master of machine learning graduates can use their skill sets in various roles and industries. Machine learning experts can find employment in robotics, data roles, software development, and more. Professionals in this field can pursue further opportunities with additional education, professional certifications , and experience.

Data Scientist

Data scientists use data sets to extract answers and insights. These employees often start with raw data and organize it into meaningful categories. Multiple industries, including finance, retail, and education, employ data scientists to collect and analyze data. Data scientists can use visualization programs to present their findings to executives.

• Required Education: Bachelor's degree
• Median Annual Salary: $100,910
• Job Outlook (2021-31): +36%

Software Developer

Software developers design software, write code, and create programs. Software developers with ML experience can incorporate their knowledge into programs to analyze data and predict user behavior. These developers can work independently or within teams.

• Median Annual Salary: $109,020
• Job Outlook (2021-31): +25%

Machine Learning Engineer

Machine learning engineers use ML for computers to solve issues and forecast possibilities. These engineers employ a combination of data and algorithms to allow computers to function with minimal human interference. Note: the salary data below applies to fewer than 50 respondents.

• Average Annual Salary, July 2023: $126,690
• Job Outlook (2021-31): Not available

Robotics Engineer

These engineers design and develop robotic systems in industries like healthcare, manufacturing, or aerospace to conduct repetitive or dangerous tasks. They may work with autonomous robots, which can benefit from AI and ML, to better manage tasks and navigate spaces.

• Average Annual Salary, July 2023: $91,120

Choosing a Master's in Machine Learning Program

Applicants researching institutions should consider multiple factors in their decision. The list below highlights some common elements for prospective students to explore.

• Accreditation : Accreditation indicates that a program or institution meets a certain standard . Schools and individual programs can receive accreditation, and a single accredited program or department at an institution does not mean that all programs within the school are accredited.
• Student-To-Teacher Ratio : A higher ratio represents more students per teacher in the university. A lower ratio can indicate smaller class sizes and more interactive time with professors.
• Cost/Financial Aid : Tuition and related fees can vary significantly between programs. A university's scholarships or financial aid may also vary.
• Location : In-state students usually pay less than out-of-state students. People who live near universities can also save on rent and living expenses.
• Reputation : Prestigious institutions have more name recognition and larger alumni networks. Graduates from these universities may have more career opportunities.

Should You Study Machine Learning Online?

According to the National Center for Education Statistics, 60.1% of students in the 2020-2021 school year attended at least one online course in their postsecondary education. Master's in machine learning programs can translate especially well to online learning due to its subject matter. Students taking online machine learning courses can learn, program, and test theories simultaneously.

Not everybody equally enjoys the online learning experience. Prospective online enrollees should consider their self-motivation, ideal learning style, and the importance of communicating face-to-face with students and professors.

Master's Degrees in Computer Science

Master's Degrees in Artificial Intelligence

Doctoral Degrees in Artificial Intelligence

Coding Bootcamps for Machine Learning

Questions about master's in machine learning degrees, what can you do with a master's in machine learning degree.

Individuals with a master's in machine learning can pursue careers such as data scientist, software developer, or AI engineer. Companies from different industries employ machine learning experts to solve complex issues.

Is a master's in machine learning worth it?

AI and ML are growing fields with career opportunities in multiple sectors. Individuals interested in technology and pursuing a career in AI/ML may find a master's in machine learning worth the cost.

Is a master's in machine learning hard?

Attendees of a master's in machine learning program should have technological and mathematical skills. Individuals that excel in these areas should find machine learning no harder than other technical programs.

How long does it take to get a master's in machine learning?

Machine learning programs have various courses and timelines. A master's in machine learning can take 1-3 years to complete.

Last reviewed June 26, 2023.

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Computer Science > Computer Vision and Pattern Recognition

Title: master's thesis : deep learning for visual recognition.

Abstract: The goal of our research is to develop methods advancing automatic visual recognition. In order to predict the unique or multiple labels associated to an image, we study different kind of Deep Neural Networks architectures and methods for supervised features learning. We first draw up a state-of-the-art review of the Convolutional Neural Networks aiming to understand the history behind this family of statistical models, the limit of modern architectures and the novel techniques currently used to train deep CNNs. The originality of our work lies in our approach focusing on tasks with a low amount of data. We introduce different models and techniques to achieve the best accuracy on several kind of datasets, such as a medium dataset of food recipes (100k images) for building a web API, or a small dataset of satellite images (6,000) for the DSG online challenge that we've won. We also draw up the state-of-the-art in Weakly Supervised Learning, introducing different kind of CNNs able to localize regions of interest. Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.

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Guideline for Students writing a Bachelor or Master’s Thesis

In the intelligent systems and machine learning (isml) group.

Status: 16.11.2020

• Finding a thesis topic: Students can choose from our list of open topics or make own suggestions. The time required for finding a topic depends on the student’s efforts and her/his familiarity with the research of the ISML group.
• Writing and submitting the thesis proposal: Writing a thesis proposal is important to specify the topic and define the goals of the thesis. This activity includes familiarization with the topic and normally requires about 2–4 weeks. The time spent for the proposal is not considered as part of the thesis. You will be registered officially upon successful submission of the proposal. 
• Starter talk: Shortly after the start of the thesis, it is common to give a short presentation in the seminar of the research group. The talk is meant to explain the topic and the goals of the thesis, and to sketch some first ideas of how to tackle these goals, so that the members of the group can give comments and provide feedback.
• Working on the thesis: In order to accomplish and successfully complete your thesis, you need to organize your time between reading relevant literature and related work, solving your own tasks, developing and implementing your solutions, and writing the thesis. As a research group, we offer you support through regular meetings with the supervisor, our students seminar, a suitable working environment, and proofreading before the official submission of the thesis.
• Submitting the thesis: At this stage, you are required to submit hard copies of the written thesis, supplemented with your source code, experimental results, a PDF of the thesis, etc., in electronic from.
• Oral defense: The defense will take place after the formal submission. Defending the thesis prior to the formal submission is only possible under specific circumstances and in agreement with the supervisors.
• Prerequisites Students interested in writing a thesis in our group should have a background in our main research topics. Normally, we assume that you attended (and successfully passed) some of our lectures (Foundations of Intelligent Systems or Data Mining in the case of Bachelor students, and Machine Learning I + II in the case of Master students). Exceptions to this rule are possible but need to be justified.
• Topics A list of open topics for Bachelor and Master’s theses can be found on the ISML website. Please have a look at this list prior to contacting Prof. Hüllermeier or any other group member (contact persons are also indicated on each thesis proposal). Ideally, you preselect two or three topics according to your preferences, and then ask for an appointment to discuss about details. Needless to say, you are also invited to propose topics yourself. In this case, please prepare a one-page summary of your main ideas.
• Supervision and co-supervision Prof. Hüllermeier will be the main supervisor for all theses written in the ISML group. Additionally, you will be assigned one of the research assistants as a co-supervisor and primary contact person. Please make sure that you meet and/or communicate with the co-supervisor on a regular basis, so as to keep her or him informed about the progress as well as any kind of difficulties that may arise. As a rough guideline, you should get in touch with co-supervisor at least every two weeks. Students are encouraged to organize their work and share it with the supervisor using the university’s GitLab service; in this way, sending emails with large attachments can also be avoided.
• Proposal Once the topic has been fixed, a proposal for the thesis needs to be prepared. This proposal is a prerequisite for registering the thesis. It is supposed to provide a motivation of the topic, to elaborate on the state of the art and prior work, and to explicate the goals of the thesis. The goals should be defined carefully, as they provide the basis for the final assessment of the thesis. The proposal must also contain a preliminary structure of the thesis and a schedule (Gantt chart). The preparation of an acceptable proposal—with appropriate assistance and in due course—is a first contribution and a main prerequisite for registration of the thesis. We reserve the right to withdraw our commitment of supervision in cases where this prerequisite is not met.
• Template The thesis should be written in LaTex and adhere to our template .
• Proofreading The co-supervisor will normally offer you to proofread a draft of your thesis, either as a whole or in parts (e.g., single chapters). The goal of proofreading is to provide general feedback and give hints for possible improvements (but not to make detailed corrections). Please note that proofreading will be offered only once for each part of the thesis; therefore, you should make sure that your draft is sufficiently mature. Moreover, please take into consideration that feedback cannot be expected on a very short notice; typically, it will take about two weeks.
• Working environment You are kindly invited to work in the PC pool of the ISML group, unless space restrictions prevent us from offering you a place equipped with a computer. Of course, if you prefer, you can also work at home or any other place. In special cases, we can support you in acquiring a virtual machine from the IMT , or to get access to HPC resources (including GPUs).
• Oral defense The thesis will end with an oral defence in the form of a seminar. The seminar will start with a presentation of the topic and main results of the thesis, followed by an informal discussion in which the audience can ask questions. The presentation should not exceed 20 minutes in the case of Bachelor and 25 minutes in the case of Master’s theses. There is no time limit for discussion.
• Working attitude Familiarization with the topic; independency, autonomy; commitment and engagement; utilization of resources (tools, computer infrastructure, etc.).
• Contents and results Documentation of related work; solution of the tasks and compliance with objectives; quality of the solutions; documentation und reproducibility.
• Written thesis Structure and readability of the thesis; length of the thesis, balance between breadth and depth; formal presentation and correctness; linguistic quality (grammar, orthography, typos, etc.); extent and quality of illustrations, figures, tables, etc.; citation and list of references (coverage, correctness, etc.).
• Defense of the thesis Quality of presentation; quality of slides; response to questions; compliance with time limit.

Further information:

• Open Theses
• Proposal Template
• Thesis Template

Writing a Thesis

Finding a thesis topic.

Students who are interested in writing a bachelor’s or master’s thesis should begin thinking about possible topics (cf. hot topics for thesis projects on this page ) or propose their own (cf. introduction to IML ). Good research questions often have their origins in scientific papers around the research topics of the IML lab. Be on the look out for new data sources that might help provide new insights into a special IML research topic.

Your Advisor and Your Committee

In order to write a bachelor’s or master’s thesis you must find an member of the IML lab who is willing to be your thesis advisor. You propose your thesis topic together with your advisor to Prof. Sonntag as the first reviewer in your committee. 

How Long Should it Be? How Long Does it Take?

A bachelor’s thesis is generally 30-60 pages, not including the bibliography. A master’s thesis is generally 60-80 pages, not including the bibliography. However, the length will vary according to the topic and the method of analysis, so the appropriate length will be determined by you, your advisor, and your committee.  Students who write a master’s thesis generally do so over two semesters, bachelor’s one semester.

Procedure and Formal Requirements

• You are a student at Oldenburg University, follow these instructions .
• You are a student at Saarland University, follow these instructions .

You must maintain continuous enrollment Oldenburg University or at Saarland University while working on the bachelor’s or master’s thesis. If you are planning to conduct interviews, surveys or do other research involving human subjects, you must obtain prior approval from DFKI.

Here you can find some theses examples .

Here you can find project group examples .

Hot Topics for Thesis Projects

You will implement novel modern approaches in computer vision such as Transfer Learning, Graph Neural Network, or Semi-Supervised Learning to solve important medical decision problems like Breast cancer detection, Chest-(X-Ray/CT) abnormalities diagnosis, or related medical domains. The target is to achieve state-of-the-art performance and the proposed method could be explainable to end users to improve the system’s reliability.

Nguyen, Duy MH, et al. “An Attention Mechanism using Multiple Knowledge Sources for COVID-19 Detection from CT Images.”,  AAAI 2021, Workshop: Trustworthy AI for Healthcare. 

Soberanis-Mukul, Roger D., Nassir Navab, and Shadi Albarqouni. “An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation.” arXiv preprint arXiv:2012.03352 (2020).

Contact: Duy Nguyen

In this topic, we will investigate important theoretical machine learning problems that have high impacts on several medical applications. It includes but is not limited to optimization formulation to incorporate efficient user’s feedback to boost the performance of trained models besides available training data (active learning), investigate benefits of transfer learning strategies when dealing with scarce data issues in medical problems, or training algorithms to adapt with highly imbalanced data distribution.

Wilder, Bryan, Eric Horvitz, and Ece Kamar. “Learning to complement humans.” arXiv preprint arXiv:2005.00582 (2020).

De, Abir, et al. “Classification Under Human Assistance.” AAAI (2021).

Yao, Huaxiu, et al. “Hierarchically structured meta-learning.” International Conference on Machine Learning. PMLR, 2019.

Requirements: Programming in Python, ideally experience with processing video and audio data

Project description: The aim is to create an annotated dataset of human-to-human dialogue in Youtube cooking videos*, that can serve as a resource for training ML models to generate conversational explanations of the cooking process. This involves the identification of videos with multiple speakers, speaker diarization (partitioning audio and/or transcript according to speaker identity), identification of conversational interaction between the speakers, and investigating if these interactions qualify as ‘conversational explanations’ of the video content

Contact: Mareike Hartmann

Relevant literature:

Speaker diarization: https://arxiv.org/pdf/2101.09624.pdf Potential videos: http://youcook2.eecs.umich.edu/explore Background on ‘conversational explanations’ from an XAI perspective: https://arxiv.org/pdf/1706.07269.pdf (Sec. 5) Note that in this project, we focus on ‘explaining’ the video content rather than model predictions.

*We focus on the process of cooking as there is some related ongoing work at DFKI, but other instructional scenarios are possible.

Requirements: Programming in Python, Pytorch, basic understanding of Deep Learning, ideally some project work on DL / CV / NLP

Project description: The student will experiment with Image Captioning, more specifically testing existing architectures on different datasets. Then, an error analysis can be conducted, in order to find out how the system can be improved.

Contact: Aliki Anagnostopoulou

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• From Show to Tell: A Survey on Deep Learning-based Image Captioning

Requirements: Programming in Python, Pytorch (or Tensorflow)

Project description: The aim of the project is to investigate how explainable NMT methods are. For example, attention weights from the Transformer architecture can be used as alignments, however it is not straight-forward which weights can be used.

• Jointly Learning to Align and Translate with Transformer Models
• Accurate Word Alignment Induction from Neural Machine Translation

Project description: The aim of the project is to investigate active learning strategies applied to relation extraction from clinical documents when using deep learning models.

Contact: Siting Liang

• BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
• Crowdsourcing Ground Truth for Medical Relation Extraction
• Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles

A central finding of preliminary research reveals that different neural network architectures, when trained on the same data distribution, generate diverse attribution maps for local explanations, supporting the assertion that attribution maps are model-dependent [2]. However, it is also understood that these attribution maps, despite their varying origins, can embody certain common characteristics [1].

Given this premise, the proposition for future research is to delve into the development of a novel algorithm that seeks to create attribution maps universally accepted by all models. These models, despite possessing diverse architectures, are based on the same data distribution. This line of enquiry will pave the way towards generating explanations that are devoid of model-dependency or model-bias, thereby privileging model-invariance.

This research aims to bridge the gap between differing neural network architectures, fostering improved communication, data interpretation, and usability. Ultimately, advancements in this field have the potential to significantly propel the evolution of explainable Artificial Intelligence (AI).

Contact: Md Abdul Kadir

[1] Kadir, M. A., Addluri, G. K., & Sonntag, D. (2023). Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency.  arXiv preprint arXiv:2307.02150 .

[2] Gupta, A., Saunshi, N., Yu, D., Lyu, K., & Arora, S. (2022). New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound.  Advances in Neural Information Processing Systems ,  35 , 33120-33133.

Colposcopy, a vital method for the diagnosis of cervical pathology, hinges primarily on the visual cues to detect abnormalities and designate regions for biopsies. The conventional method often includes the use of Acetic acid (5%) for highlighting the cells’ nucleus and hence revealing abnormal or pre-cancerous cells, while green filters aid in visualizing blood vessels supplying these regions. However, vast variations in individual practitioner’s experience and expertise may lead to ununiformed assessments.

This research proposal aims to bridge this gap introducing deep learning algorithms, which have shown unprecedented success in image recognition and classification tasks, into colposcopic examinations [1]. The utilization of these machine learning methodologies could allow automatic detection of cancerous or precancerous regions in colposcopic images or videos, automating and standardizing the evaluation process while offering real-time feedback and suggestions during the examination.

[1] Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, Subramaniam U, Manoharan S. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. Biomed Res Int. 2021 May 4;2021:5584004. doi: 10.1155/2021/5584004. PMID: 33997017; PMCID: PMC8112909.

Bachelor and Master Thesis Opportunities

Finding a supervisor.

Our group supervises as many students as possible, but we often do not have the capacity to supervise all interested students.

Preliminaries

Prerequisites for thesis candidates.

Applicants must have significant experience in machine learning, e.g., as acquired through courses offered by our group (passed with a grade of “good” or better) or an equivalent qualification. This often includes a deep conceptual understanding of machine learning and significant programming experience. The necessary skills vary depending on the thesis topic, e.g., purely theoretical theses require more mathematical than programming skills. Candidates must possess the necessary abilities before applying. Cooperation with an external party (like a company or research institute) is usually only possible when the collaborating party does not significantly reduce scientific autonomy (e.g., regarding data access, topic selection, and thesis trajectory).

Application form

Interested students can contact us with the thesis application form as a PDF, a curriculum vitae or resume, and a cover letter. Please provide evidence of relevant skills. Degree theses are often connected to ongoing research in our group. However, it is also possible for students to suggest their own topics/ideas.

Application channels

1. contacting researchers directly.

Candidates can apply to potential supervisors directly using the standardized form above. In your email, please detail your reasons for reaching out to the particular individual.

How to find suitable researchers:

• Filter researchers by interests using this platform .
• Browse our team overview and research profile .
• Contact the researchers from the section “Thesis supervision offerings” below.

Students may send a follow-up email if a researcher does not respond, as messages can sometimes be overlooked amidst numerous emails. Please wait at least two weeks before writing such a follow-up mail.

2. Applying via a mailing list

In addition to targeted applications via channel (1), candidates can send their application to thesis(∂)ml.tu-berlin.de using the standardized form above. This distribution list reaches most researchers in our group. Responses will be provided only if your application aligns well with the needs of at least one researcher in our group.

3. Contacting the Thesis Coordination Team

If previous application attempts via the outlined channels are unsuccessful, and you meet the necessary thesis candidate prerequisites, you are encouraged to contact the Thesis Domain Coordinators. Please include a brief summary of your prior application attempts in your communication.

Thesis Domain Coordinators:

• Quantum Chemistry / ML for Physical Sciences: Stefan Chmiela
• Explainable AI: Grégoire Montavon
• Probabilistic ML: Shinichi Nakajima
• Biomedical Sensing and Neurotechnology: Alexander von Lühmann
• Kernel Methods: Andreas Ziehe
• Computational Neuroscience: Mina Jamshidi Idaji
• Digital Pathology: Julius Hense
• Multimodal Learning: Jannik Wolff
• Anomaly Detection: Alexander Bauer
• NLP and Digital Humanities: Oliver Eberle
• Graph Neural Networks: Thomas Schnake

For topics not covered by the above domains, or for general inquiries and feedback regarding the application process, please direct your correspondence to thesis.coordination(∂)ml.tu-berlin.de. If students do not receive a reply from the Thesis Domain Coordinators or the mailing list, they may send a follow-up email after a waiting period of at least two weeks.

Thesis supervision offerings

Please be aware that we often keep the titles of available theses confidential, as they frequently pertain to our current, yet-to-be-published research. Group members can access our internal database .

High-level overview of inquiring researchers

Publicly advertised thesis topics, next steps after having found a supervisor.

General information (link)

After having found a supervisor, students prepare a thesis proposal that includes

• the research question and its context/motivation,
• related work,
• preliminary methodological and/or experimental results,
• and formalities such as the number of ECTS credits and the writing time specified in the student’s examination regulations.

The supervisor can help the student with writing the proposal. Students may register their thesis with the examination office once Prof. Müller or an authorized senior researcher approves the proposal. We encourage students not to underestimate the time required for writing the proposal. Furthermore, consider that we may require some time to review the proposal. Therefore, it is helpful to apply to our group early.

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Machine Learning - Master

Key data on study program.

Faculty Science

Academic Degree Master

Restricted admission None

Regular duration of studies 4 semester

Is a combination subject required? No

Application deadline for Germans and EU citizens  Winter semester: 30.04

Application deadline for non-EU citizens Winter semester: 30.04

Valid for all applicants with German or EU university entrance qualification.

Program start Winter semester

Language of instruction English

Costs Tuition and other fees

Requirements and application

On what criteria are applicants selected.

Admission to the international Master’s program in Machine Learning is not admission-restricted, but all students, including those holding a Bachelor’s degree in Computer Science, are requested to submit a formal application. However, you need to apply  in order to establish whether you meet the general requirements.

Application deadline for international (non-EU) citizens as well as German and EU citizens: 30 April

Further information and answers to the most common questions can be found on the website of the Master's program and on the FAQ page .

Which requirements must I meet for studies?

A Bachelor's degree (or equivalent) in either computer science, mathematics, physics or related natural sciences with a grade of at least 2.3 (German grading system). In particular, competences from the following areas are required, equivalent in content and scope to those in the BSc course in Computer Science in Tübingen:

Mathematics: one- and multidimensional analysis, linear algebra and either numerics or stochastic

Computer science: Programming, Algorithms and Data Structures.

Applicants must also provide adequate proof of English proficiency.

Further information & FAQ regarding the admission requirements

Which languages must I know?

Courses are held in English. Applicants must provide adequate proof of English proficiency, documented by one of the following documents:

German Abitur certificate with proof of 6 (G8) or 7 years (G9) of English language instruction

TOEFL iBT test with at least 94 points

IELTS test with a score of at least 7.0

Cambridge Certificate in Advanced English (CAE)

Higher education entrance qualification from Great Britain, Ireland, USA, Canada, Australia, New Zealand

Which other subjects can I combine with my main subject?

The Machine Learning program cannot be complemented with a minor subject.

Program details

Which topics are dealt with in the program.

The international Master’s program in machine learning offers a wide choice of courses in computer science and, apart from a few mandatory courses, allows students to choose their subjects according to interest. Students will attend lectures, seminars, and project lab courses under the supervision of scientists who introduce them to basic and applied research and current topics in machine learning.

To pick up on scientific trends and make the best use of the current state of research, the curriculum relies heavily on the strong research presence on site, in machine learning as well as the wider field of computer science: top-level researchers in all major methodological branches of machine learning are present in Tübingen – personnel that will actively engage in teaching for the Master’s Program Machine Learning. Since the field is obviously very young and currently developing extremely rapidly, training will naturally be based on the most recent insights and the most pressing research questions of these teaching researchers. Project work and the Master’s thesis will offer students the opportunity to develop code for research purposes and their own scientific projects.

As interdisciplinarity is an important aspect, the Master’s thesis can be supervised by a professor from any subfield of computer science.

What is special about the Tübingen program?

The University of Tuebingen collaborates closely with the  Max Planck Institute for Intelligent Systems and the Max Planck Institute for Biological Cybernetics and they form together one of leading research hubs for machine learning in Europe and world-wide. This manifests in several recent initiatives:

the Cyber Valley ,

the Excellence Cluster `Machine Learning: New Perspectives for Science' ,

the TUE AI Center (BMBF Competence Center for Machine Learning)

the International Max Planck Research School for Intelligent Systems .

These initatives provide plenty of opportunities for research as well as industry contacts for the students of our master program.

How is the program structured?

The course consists of four major study areas: Foundations of Machine Learning, Diverse Topics in Machine Learning, General Computer Science and Expanded Perspectives. In the first semester Mathematics for Machine Learning, and Data Literacy are recommended modules, followed by Statistical Learning, Probabilistic Inference and Learning and Deep Learning in the next semester. At least one practical course and one seminar are recommended for the second or third semester. The master thesis is recommended for the fourth semester.

The Master’s program in machine learning is research-oriented and will enable graduates to analyze, implement, leverage, and modify techniques of machine learning. Education in problem solving capabilities is a central training objective.

Students have the opportunity to advance their knowledge and skills to a level which will allow them to get involved in top national and international research in bioinformatics and its application to the life sciences. This includes a critical discussion of research goals, contents, proposals, and research problems, and requires a high scientific level.

Is study abroad expected?

Information on the Erasmus program (Uppsala, Schweden) for students of computer and cognitive science

Optional time abroad You may choose to spend some time abroad as part of any study program. You should start planning it 12 to 18 months before your departure.  Here you will find general information and advice on studying abroad . In addition, some departments have their own subject-specific services providing information on studying abroad.

What else do I need to know?

Computer science in Tübingen is one of the leading CS departments in Germany in terms of research, and it is internationally renowned. The department collaborates with major enterprises and research institutes both nationally and internationally. The CS department places strong emphasis on interdisciplinary cooperation with other university departments, in particular medicine, biology, psychology, and media studies, and also with the three Max Planck Institutes located in Tübingen.

Tübingen is part of the Stuttgart metropolitan area - one of the strongest economical regions in Europe with a thriving local automotive and IT industry. Tübingen and its surrounding region (including Reutlingen, Böblingen, Sindelfingen and the Neckar-Alb) is the location of many university spin-offs, startups, medium-sized and large companies, with a high demand for computer scientists.

In addition to excellent future prospectives, this region offers numerous opportunities for student jobs.

In the winter term 2023/24 about 2000 students study at the Department of Computer Science:

Module handbook

Module handbook Machine Learning

The module handbook describes the goals and structure of a degree program. In the module handbook you will find details of the type of modules offered and their respective workloads as well as the classes, requirements and assessment. An ideal course plan is set out in the module handbook to help you plan your studies. The module handbook and the exam regulations help you to understand the framework and conditions of your program, and enable you to choose the right modules/ classes.

Exam regulations

You can find a formal description of the degree here .

The exam regulations contain the legally binding regulations for a degree program. They set out, for instance, which modules must be taken and which assessment must be completed in order to successfully complete the degree program. The exam regulations also regulate how often exams may be repeated, which deadlines apply to assessment, and how the final grade is calculated. The exam regulations and the module handbook help you to understand the framework and conditions of your program, and enable you to choose the right modules/ classes.

Help with choosing a study program

Is the program right for me, help with your decision to study in tübingen:.

To help you decide whether Tübingen is right for you, here are some contact points and information on studying in Germany:

University of Tübingen

• University of Tübingen’s international profile  
• Contact: University office for the Advising and Admission of International Students
• Information on application for international students  
• FAQs for international students  
• Addresses and contact points for international students at the University of Tübingen  

General information

• Study and conduct research in Germany (DAAD information)  
• All degree programs in Germany
• Find scholarships   

After your studies

Career prospects.

Due to very fast technological developments in handling large amounts of data and apply findings in a wide variety of applications, there is an ever growing need for specialists in machine learning. Since this is a highly demanding area, a PhD degree is often required. Graduates in this international Master's program will be competent in all basic and many advanced areas of machine learning, understanding and suitably applying this increasingly essential tool for dealing with large datasets, be it in science, industry or alternative domains. The studies program deals both with generic methods and their applications to specific fields, making it highly relevant for new career and job market purposes. In this whole Master program, besides professional expertise, graduates will also acquire language skills and intercultural competence due to the program’s international nature – another requirement of the international job market, both in academia and without.

Advice and internships The University’s Career Service assists with careers orientation and getting started in your profession . Jobs and internships can be found in the Praxisportal .

Doctorate options

The master program qualifies for doctoral studies in Computer Science. For further information see the website  Faculty of Science - Doctoral Studies .

Join the alumni society

Information about the  Alumni of Tübingen .

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• Professorship of Machine Learning
• TUM School of Computation, Information and Technology
• Technical University of Munich

Masters and Bachelors theses and research internships

If you are interested in a Masters or Bachelor thesis project or a research internship (Forschungspraxis) in our group we are happy to propose a concrete problem related to our current interests. Our group focuses on machine learning and optimization, deep learning for inverse problems, and DNA data storage and DNA information technologies. To get an idea about our current research, please check out our recent papers at google scholar . Projects usually involve a mixture of theory and applied work and require strong interest documented by excellent grades in relevant subjects such as linear algebra, probability and statistics, machine learning, signal processing, optimization, or related courses.

Below is a list of open topics. If you are interested in one of the projects, please send an email directly to the superviser and cc  [email protected] . Include your transcript of records from TUM and your curriculum vitae and the planned start and end dates.

The list of topics is sometimes incomplete, and we are also happy to propose other topics if there is a good fit. If you are interested in a topic related to our current interests that is not listed below, please reach out to  [email protected]  and again include your transcript of records and CV.

External projects:  If you are planning to carry our our project externally, for example at a company or another university, and you want us to supervise the project, please send an email to  [email protected]  including the name and contact of the exernal supervisor, your transcipt of records and CV, and a project description and an explanation how the project is related to our expertise. We can only supervise external projects if they are related to our expertise and current research interests.

Thesis and project administration and logistics:  Here are some guidelines on the thesis and research project administration and on the grading.

Open projects

Robust fine-tuning of deep learning models for accelerated MRI reconstruction

We’re looking for a Master’s student to explore robust fine-tuning strategies for pre-trained deep learning models for accelerated MRI. While fine-tuning improves performance on the target dataset, it sacrifices robustness to out-of-distribution data. This project aims to better understand fine-tuning for image reconstruction and in particular accelerated MRI with the goal of proposing robust fine-tuning methods for accelerated MRI. Supervisor:  Kang Lin ( [email protected] ) Prerequisites:  Strong coding skills with PyTorch. A good understanding of deep learning frameworks. Type of project: Reserach Internship (Forschungspraxis)

Deep learning based fetal magnetocardiography system

We are looking for a Master’s student to work on deep learning based reconstruction for a fetal magnetocardiography system, in a interdisciplinary collaboration with Prof. Fierlinger and Prof. Wacker-Gussmann. The goal of the thesis is to develop deep learning based signal reconstruction techniques for this new medical imaging and sensing technology. Supervisor:  Reinhard Heckel, Kang Lin Prerequisites:  Strong coding skills, specially in pytorch. A good understanding of deep learning for inverse problems. Type of project:  Masters thesis

Self-supervised reconstruction of PET Scans

We are looking for a Master's student to work on reconstructing PET scans using self-supervised deep learning techniques. Since obtaining ground truth PET images is difficult and expensive, self-supervised training has emerged as a useful tool. In other medical image domains such as CT and MRI self-supervised techniques have proven to be successful. Our aim is to test those techniques on PET imaging. To learn more about the project, please contact Youssef Mansour. Supervisor: Youssef Mansour ( [email protected] ) Prerequisites: Strong coding skills, specially in pytorch. A good understanding of deep learning for inverse problems. Type of project: Masters thesis

A Test-Time-Training Approach for Image Denoising under Distribution Shifts

We are looking for a Master's student to work on a project of improving a method that finetunes a trained model at test time to adapt to different distribution shifts for image denoising. The different shifts include natural and medical images, synthetic noise, and real-world camera and microscope noise. Your task is to understand and reproduce the method as as well adding ablation studies and extra comparisons with other baselines. You will also contribute to improving the performance of the method, and making it more intuitive. To learn more about the project, please contact Youssef Mansour. Supervisor: Youssef Mansour ( [email protected] ) Prerequisites: Strong coding skills, specially in pytorch. A good understanding of deep learning frameworks. Interest in collaborative work. Type of project: Masters thesis or Reserach Internship (Forschungspraxis)

Estimating the age of a DNA sample from sequencing data 

We are looking for a Master’s student to develop, implement, and test an idea to estimate the age of a given DNA sample from sequencing data alone. First you would perform literature research on existing approaches and then implement and test a few concrete ideas in python. Supervisor: Reinhard Heckel ( [email protected] ) Prerequisites:  Interest in interdisciplinary work and data analysis. Type of project: Masters thesis

Cardiac magnetic resonance imaging with neural networks 

We’re looking for a Master’s student to work on a new deep neural network based method for imaging a non-static object, specifically a beating heart. You would implement an idea for a neural network based method in pytorch, evaluate the method on real data within a collaboration with the University Hospital, and there is also room for developing your own ideas. Supervisor: Reinhard Heckel ( [email protected] ) Prerequisites:  Ideally knowledge of deep learning for inverse problems and python and pytorch programming experience. Type of project: Masters thesis

Current and past theses in the group

Jakub Dvorak, ``Simultaneous Self Supervised Image Denoising and Deconvolution’’, Project/Forschungspraxis, ongoing

Oliver Kovacs, ``Developing a VarNet for DeepDeWedge’’, Project/Forschungspraxis, ongoing

Serdar Caglar, ``Test time training for denoising distribution shifts’’, Project/Forschungspraxis, 2024

Andreas Faika, ``Comparison of Tokenizers for LLMs’’, Project/Forschungspraxis, 2024

Raimundo Parra, ``Unintentional Bilingualism in Large Language Models’’, Project/Forschungspraxis, 2024

Mamdouh Aljoud, ``Filtering techniques in next generation multimodal datasets’’, Master's thesis, 2023

Cheng Yan, ``Meta-Learning for mulit-task MRI reconstruction’’, Master's thesis, 2023

Francesco Bollero, ``Data pruning for image reconstruction’’, Master's thesis, 2023

Faidra Patsatzi, ``Randomized smoothing for inverse problems’’, Bachelor's thesis, 2023

Xiaodong Lei, ``Adversarial robustness of deblurring methods’’, Project/Forschungspraxis, 2023

Dogukan Atik, ``Scaling laws for self-supervised image denoising’’, Master's thesis, 2023

Mamdouh Aljoud, ``Deep networks for nanopore basecalling’’, Project/Forschungspraxis, 2023

Rafael Vorländer, ``Reimplementing CryoGAN’’, Project/Forschungspraxis, 2023

Juan Cao, ``Uncertainty quantification methods for compressed sensing’’, Project/Forschungspraxis, 2023

Litao Li, ``Datawork for accelerated MRI’’, Master's thesis, 2023

Guang Chai, ``Evaluating deep-learning based imaging systems’’, Master's thesis, 2023

Johannes Kunz, ``Generative models for Cardiac Magnetic Resonance Imaging’’, Master's thesis, 2023

Ali Can, ``Understanding the Contribution of Training Samples on a Prediction of a Single Test Image in a Denoising Task Using Attention Mechanism’’, Project/Forschungspraxis, 2022

Deniz Uysal,  ``Spectral Computed Tomography Image Reconstruction’’, Master's thesis, 2022

Xuyang Zhong, ``Self-Supervised Learning for Image Denoising’’, Master's thesis, 2022

Johannes Kunz, ``Dynamic MRI reconstruction’’, Project/Forschungspraxis, 2022

Weixing Wang, ``Graph neural networks for clustering and aligning DNA sequences for DNA storage'', Project/Forschungspraxis, 2022

Yundi Zhang, ``Coordinate-based image priors'', Master's thesis, 2022.

Samuel Eadie, ``Rate-Distortion Stochastic Autoencoding for Robust Representation Learning and Out-of-Distribution Detection'' (carried out at Bosch Research), Master's thesis, 2022

Kang Lin, ``Transformers for image recovery'', Master's thesis, 2021.

Frederik Fraaz, ``Image recovery with invertible neural networks'', Master's thesis, 2021.

Youssef Mansour, ``Neural network architectures for image recovery and denoising'', Master's thesis, 2021.

Benedikt Böck, ``Multiplicative filter networks for image processing applications'', Project/Forschungspraxis, 2021

Mohamed Ketata,  ``Data standardisation, multi-domain learning, and artifact robustness for improved MRI'', Bachelor's thesis, 2021.

Deniz Uysal, ``A simple encoder and decoder for DNA data storage with Polar codes'', Project/Forschungspraxis, 2021.

Yundi Zhang, ``Deep matrix decoder for collaborative filtering'', Project/Forschungspraxis, 2021.

Youssef Mansour, ``Ensembles of image reconstruction method for MRI'', Project/Forschungspraxis, 2021.

Jacob Geussen, ``Diffusion MRI denoising with neural networks'', Bachelor's thesis, 2020.

Lena Heidemann, ``FastMRI with untrained neural networks'', Master's thesis, 2020.

Tobit Klug, ``Image separation with untrained neural networks'', Master's thesis, 2020.

Oleksii Khakhlyuk, ``Convolutional neural networks with fixed kernels'', Bachelor's thesis, 2019. 

Zi Yang, ``Probabilistic matching networks for few-shot learning'', Master's thesis, 2019.

The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an  introduction , which presents a brief overview of the topic and the  research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging:  A deep learning approach to improve the accuracy of medical diagnoses.

Introduction:  Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction:  Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction:  Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction:  Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction:  Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by  Joanna Kosinska  on  Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction:  Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction:  Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction:  Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction:  Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction:  Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by  UX Indonesia  on  Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction:  Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction:  Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction:  Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction:  Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction:  Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by  Windows  on  Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction:  Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction:  Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction:  Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction:  Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction:  Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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MIT Libraries home DSpace@MIT

• DSpace@MIT Home
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This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

MIT Theses are openly available to all readers. Please share how this access affects or benefits you. Your story matters.

If you have questions about MIT theses in DSpace, [email protected] . See also Access & Availability Questions or About MIT Theses in DSpace .

If you are a recent MIT graduate, your thesis will be added to DSpace within 3-6 months after your graduation date. Please email [email protected] with any questions.

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MIT Theses may be protected by copyright. Please refer to the MIT Libraries Permissions Policy for permission information. Note that the copyright holder for most MIT theses is identified on the title page of the thesis.

Theses by Department

• Comparative Media Studies
• Computation for Design and Optimization
• Computational and Systems Biology
• Department of Aeronautics and Astronautics
• Department of Architecture
• Department of Biological Engineering
• Department of Biology
• Department of Brain and Cognitive Sciences
• Department of Chemical Engineering
• Department of Chemistry
• Department of Civil and Environmental Engineering
• Department of Earth, Atmospheric, and Planetary Sciences
• Department of Economics
• Department of Electrical Engineering and Computer Sciences
• Department of Humanities
• Department of Linguistics and Philosophy
• Department of Materials Science and Engineering
• Department of Mathematics
• Department of Mechanical Engineering
• Department of Nuclear Science and Engineering
• Department of Ocean Engineering
• Department of Physics
• Department of Political Science
• Department of Urban Studies and Planning
• Engineering Systems Division
• Harvard-MIT Program of Health Sciences and Technology
• Institute for Data, Systems, and Society
• Media Arts & Sciences
• Operations Research Center
• Program in Real Estate Development
• Program in Writing and Humanistic Studies
• Science, Technology & Society
• Science Writing
• Sloan School of Management
• Supply Chain Management
• System Design & Management
• Technology and Policy Program

Collections in this community

Doctoral theses, graduate theses, undergraduate theses, recent submissions.

Intracellular sensor spatial multiplexing via RNA scaffolds 

Conducting polymers for electrochemically mediated separations 

Mechanistic insight on a chimeric Cas9 protein's specificity for DNA target with 5 '-NAA-3' PAM 

Marjolein Bolten, succesfully defended her Master thesis. Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods

Bolten, Marjolein (2024)  Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods.  University of Twente, Enschede. 

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