The lecture (LSF) will be taught in English and addresses Master and PhD students in Mathematics or related fields. We will use a hybrid format, with videos of the lectures that can be asynchronically assessed and on-site inverted classroom lectures and practical exercises.
Registration and first (virtual) meeting
The lecture and exercises time slots have now been allocated (LSF). Please note that the first lecture takes place on Tuesday, October 11, 11h15 in room G05-211 and can be assessed via this zoom link with the password omml. If you intend to participate only virtually (e.g., our students from Belarus), this will also be possible using the above zoom link. Details will be discussed on Tuesday, October 11. In this first meeting all organizational questions will be discussed. For communication, we will be using the MatterMost group of the lecture. I advise you to register directly if you have not done so using this link.
Exercises and Downloads
Will be made available with a link from this website.
- Lecture with 4+2 SWS and 9 ECTS-Credits
- Time slots can be discussed and maybe shifted during the first meeting. Currently allocated are Tue 11h15-12h45 (G05-211) and Fri 13h15-14h45 (G05-117). The practical exercises are currently on Wed 7h15-8h45 (G05-117).
- Lecturer practical exercises and
It will survey optimization problems in and methods for machine learning.
A preliminary table of contents is the following.
- Introduction to Machine Learning
- Machine Learning Case Studies: text classification and perceptual tasks
- Problem Definitions and Methods Overview
- Efficient Calculation of Derivatives
- Problem reformulations
- Stochastic Gradient Methods
- Noise Reduction Methods
- Second-Order Methods
- Other Popular Methods
- Ethical, Philosophical, and Economical Aspects of Artifical Intelligence
Practical exercises will complement the lecture and focus on KERAS and Python scikit-learn.
Mathematical basics (Analysis and Linear Algebra) and programming skills. Introduction to Optimization. The lecture Nonlinear Optimization is highly recommended, but not absolutely necessary.
Module descriptionThe lecture is a master lecture in the mathematics curriculum and described in the module handbook (currently page 31) as a Wahlpflicht module:
- WPF MA (Module 12, 13, 14)
- WPF MA;M 1-3 (Module M3D)
A translation of the module description:
- Goals and competences: The students acquire competences with respect to modeling and algorithmically solving optimization problems that are at the basis of modern machine learning techniques. A rigorous mathematical analysis of convergence theory and implementation aspects of different algorithms is the guiding theme of the lecture. In the exercises the students learn how to implement algorithms efficiently on a computer and to apply them to concrete problem instances.
- Content: An introduction to mathematically formulating machine learning problems in a generalized way, calculating derivatives, stochastic and deterministic derivative-based algorithms, convergence theory. See above for a table of contents.
The lecture is also open to other master and PhD students of OVGU. In particular, there is an agreement that ORBA students may choose the lecture as a Wahlpflicht (with 10 CP to motivate the independent study of mathematical foundations necessary to follow the lecture). However, please note that the lecture is addressed to mathematical master students and assumes a good understanding of mathematical basics, especially in the second part of the lecture. If you are mainly interested in applying machine learning and not so much in analyzing the training process, other lectures might be better suited for you. Note that the lecture Concepts and Algorithms of Optimization is not sufficient as a requirement, you will have to invest more time to acquire additional mathematical knowledge.
Feel free to send me an email with general questions: