IE 675b: Machine Learning (HWS 2025)
Organization
- Lecturer: Prof. Dr. Rainer Gemulla
- Tutors: Julie Naegelen
- Type of course: Lecture, exercises, assignments (9 ECTS points)
- Prerequisites: IE 500 Data Mining I (recommended / in parallel), knowledge of probability and statistics
- Registration: enroll in Portal 2
The lecture (2 sessions per week) and tutorial (1 session per week) will be held in-presence, both starting in the first week (tutorials on Wednesday). Details are discussed in the kickoff lecture (Tuesday, Sep 2, 10:15).
Content
Machine learning is concerned with building computer systems that improve with experience as well as the study of learning processes, including the design of algorithms that are able to make predictions or extract knowledge from data. The aim of this module is to provide an introduction into the field of machine learning, and study algorithms, underlying concepts, and theoretical principles.
- Basics of machine learning
- Training, inference, decision
- Generative models (e.g., Beta-binomial, Naive Bayes, PPCA)
- Discriminative models (e.g., logistic regression, SVM, GPR)
- Latent variable models (e.g., SVD, LLM, GMM)
- The EM algorithm
- Kernel methods
- Hyperparameter optimization
Deep learning and more advanced topics are covered in the follow up course "IE 678 Deep Learning" (and others).
Lecture Notes
Lecture notes, exercises, assignments, and supplementary material can be found in ILIAS.
Literature
- K.P. Murphy. Probabilistic Machine Learning: An Introduction. The MIT Press, 2022 (https://probml.github.io/pml-book/book1.html)
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. The MIT Press, 2017 (http://www.deeplearningbook.org/)
- Additional material and articles provided in lecture notes