Due to the technological advances in hardware and compute power nowadays the visual data is collected in huge amounts practically by everyone. The digital cameras are pervasive. Most of the smart devices include camera sensors and are capable of capturing stills and videos. With these large amounts of visual data there is a pressing need to automatically process, organize, interpret the data and refine the information in ways that humans are capable. Computer Vision is asked to provide such solutions. How to achieve such goals? What are the challenges? What are the solutions? What is state-of-the-art? What are the benefits? What is the societal impact? How can we further improve the state-of-the-art? In this seminar, we review, explore and debate such questions based on the recent research at the intersection of Machine Learning, Computer Vision, Robotics and Artificial Intelligence. Each participant will be assigned one topic from a range of computer vision topics, including but not limited to those listed below. Each participant is expected to prepare a written review report covering the state-of-the-art on the particular topic (compiled from at least two research papers) and a corresponding oral presentation. At the same time, each participant is expected to interact, read and comment on the reports provided and presented by the other participants. Each participant will get skills on critical analysis, scientific discourse, and preparation, writing, and presentation on a research topic. Moreover, the participants will get acquainted with state-of-the-art computer vision.

Machine Learning for Mobile Health

Course Description

Welcome to Machine Learning for Mobile Health, an advanced course focusing on the integration of machine learning techniques and technologies with mobile applications for the advancement of healthcare solutions. This comprehensive program covers critical topics such as Mobile Crowd Sensing, Digital Health Apps, Mobile Application Engineering, Machine Learning Pipelines using CRISP-DM, Statistics and Populations, Data Preparation, Feature Engineering, Tree-based Machine Learning Models, Machine Learning Metrics, Cross-Validation, Artificial Intelligence (AI) Explainability, AI Ethics, Regulations, and Statistical Method: Linear Component Analysis.

Course Outline

1. Introduction to Digital Health and Mobile Applications

   • Overview of digital health technologies
   • The role and importance of mobile applications in healthcare
   • Ethical considerations and regulatory frameworks for digital health apps

2. Mobile Crowd Sensing and Digital Health Apps

   • Understanding Mobile Crowd Sensing and its applications in healthcare
   • Designing and implementing digital health applications

3. Mobile Application Engineering

   • Principles and techniques for mobile application development
   • UI/UX design considerations for health-focused applications
   • Security and privacy in mobile app engineering

4. Machine Learning Pipelines with CRISP-DM

   • Overview and implementation of the CRISP-DM framework
   • Data collection, cleaning, and preprocessing
   • Model selection, training, and evaluation in a pipeline

5. Statistics and Populations in Healthcare

   • Understanding statistical methods relevant to healthcare
   • Analysis of populations and healthcare data
   • Application of statistical tests for healthcare research

6. Data Preparation and Feature Engineering

   • Techniques for data preparation for machine learning
   • Feature engineering for enhancing model performance
   • Handling imbalanced data and missing values

7. Tree-based Machine Learning Models

   • In-depth study of tree-based machine learning algorithms (e.g., Decision Trees, Random Forest, XGBoost)
   • Optimization and hyperparameter tuning for tree-based models

8. Machine Learning Metrics and Cross-Validation

   • Understanding and using metrics for model evaluation (classification and regression)
   • Cross-validation techniques for robust model assessment

9. Artificial Intelligence: Explainability and Ethics

   • Techniques for explaining AI models and decisions
   • Ethical considerations and responsible AI practices in healthcare
   • Compliance with regulations in AI for healthcare

10. Statistical Method: Linear Component Analysis

    • Introduction to Linear Component Analysis (LCA)
    • Understanding its applications in healthcare data analysis
    • Implementing LCA for healthcare research

This course will equip participants with the skills and knowledge to effectively leverage machine learning techniques in mobile health applications, contributing to the enhancement of healthcare services and outcomes. Join us in this exciting journey at the intersection of machine learning and mobile health!

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

The discovery of the cells changed our perspective on the underlying structure of tissues and organs. We started to understand that alterations in the cells of an organism could have a causal link to diseases, and we started to catalog human diseases accordingly.

Now, we have in our hands, new high-throughput technologies enable us to unravel the underlying genetic and epigenetic regulation of the biological systems, coupled with imaging and spatial information at the “single-cell” resolution.

These technologies generated a huge and high-dimensional data which gave us exciting challenges to develop computational approaches for data analyses and integration.

This course will be an exciting learning journey to the data science of the single-cell genomics. You will learn about the state of the art deep learning architectures which have been recently developed in our field. This course will help you to gain the know-how skills and knowledge to be part of this new and revolutionary understanding of the biological systems in health and disease.

More Info: https://www.abdelrahman-mahmoud.com/teaching/

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

The course aims to provide an introduction to Artificial Intelligence (AI) by covering the main topics ranging from search and logic, to decision-making and learning. The course is highly recommended to whomever is interested in the exciting topic of AI for the current studies, and possibly for a future job in research or industry.

The course is organized into 15 lectures with the following topics:

Lecture 00: Introductory remarks;
Lecture 01: Intelligent agents;
Lecture 02: Classical search;
Lecture 03: Advanced search;
Lecture 04: Propositional logic;
Lecture 05: First-order logic;
Lecture 06: Knowledge representation;
Lecture 07: Planning;
Lecture 08: Uncertainty quantification and probabilistic reasoning;
Lecture 09: Probabilistic reasoning over time;
Lecture 10: Decision theory;
Lecture 11: Game theory;
Lecture 12: Machine learning;
Lecture 13: Deep learning;
Lecture 14: Reinforcement learning;
Lecture 15: Demo exam.

At the end of the course, the students will have acquired the required theoretical and empirical knowledge about classic and modern Artificial Intelligence (AI) approaches. They will have a good knowledge of the core problems and goals of AI, its current state-of-the-art, and its future challenges. They will be able to conduct their M.Sc. theses on topics related to AI, and they will have good starting knowledge of AI for a future career in research or industry.

The evaluation consists of a written exam.
The exam is in English and lasts 2 hours. There will be an exam at the end of the Winter Semester and one at the beginning of the Summer Semester. It will consist of open questions and/or quick application of algorithms by hand.

The course is organized into 15 lectures and 15 exercise sessions. The lectures will provide both theoretical and practical perspectives on the respective topics. The exercise sessions will aim at strengthening the knowledge of the topics presented in the lectures through a quick refresh and guided exercises. Some of the exercise sessions will be used to host invited lectures given by experts about cutting-edge topics of AI, e.g., Robotics and Virtual Reality, explaining them more in detail compared to the lecture. Through the expert knowledge of the invited speakers, the invited lectures aim to provide a rich knowledge of the topics that will shape the future of AI, with a particular focus on research aspects and their impact on society.

IMPORTANT: This course is mutually exclusive with "Künstliche Intelligenz 1", since they overlap significantly. This means that students can register either for the exam of this course or for "Künstliche Intelligenz 1". This course also has a slight overlap with "Künstliche Intelligenz 2", but students are allowed to register for both these exams if desired.
Students are also allowed to follow this course and have it registered as "Künstliche Intelligenz 1", if needed.

The main reference book for the course is:
- Stuart, Russell, and Norvig Peter. "Artificial Intelligence A Modern Approach Third Edition." (2010). (https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf).

Additional references are:
- Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006. (https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf);
- Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009;
- Peterson, Martin. An introduction to decision theory. Cambridge University Press, 2017;
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. (http://incompleteideas.net/book/the-book.html).