The lecture (LSF) will be taught in English and addresses Master and PhD students in Mathematics or related fields. We will use a virtual format, with videos of the lectures that can be asynchronically assessed and online practical exercises and discussions using Zoom.
Registration and first (virtual) meeting
The lecture and exercises time slots have now been allocated (LSF). We are going to have a first virtual Zoom meeting on Tuesday, October 27, 13:15. Use the password 2020omml to enter. In this first meeting all organizational questions will be discussed, among others which of the two available time slots on Thursday shall be used for the practical exercises.
You do not need to register officially, but it would be helpful if you could send an email to specifying your name, your curriculum (Master, PhD in ...), your Immatrikulationsnummer, and your email, this would be very helpful to set up invitations to MatterMost and to a computing server. Please send this email before October 26.
Exercises and Downloads
- Lecture with 4+2 SWS and 9 ECTS-Credits
- Time slots: Tue 13:15 - 14:45 for discussions and questions related to the lecture
- Time slots: Thu 9:15 - 10:45 or Thu 17:00 - 18:30 for practical exercises (yet to be decided)
- Lecturer practical exercises
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: