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17165-01 - Lecture: Machine Learning (8 CP)
| Semester | spring semester 2026 |
| Further events belonging to these CP |
17165-01 (Lecture) 17165-02 (Practical course) |
| Course frequency | Every spring sem. |
| Lecturers | Volker Roth (volker.roth@unibas.ch, Assessor) |
| Content | Probabilities Generative models for discrete data Classification & regression: Frequentist & Bayesian approaches, model selection, sparse models Neural networks: Feed-forward & recurrent topologies, encoder-decoder models, interpretability in deep learning models Elements of statistical learning theory Support Vector Machines and kernels, Gaussian processes Mixture models, mixtures of experts Linear latent variable models: Factor analysis, PCA, CCA Non-linear latent variable models: Variational autoencoders, deep information bottlenecks, sequence-to-sequence models, large language models |
| Learning objectives | Understand the theoretical foundations of Machine Learning Understand and apply practical learning algorithms: (generalized) linear models for regression and classification, Gaussian processes, elements of statistical learning theory, mixture models, clustering, (deep) neural networks, (traditional and large) language models. Program in Python. PyTorch & Tensorflow |
| Bibliography | https://mitpress.mit.edu/books/machine-learning-1 https://www.deeplearningbook.org/ |
| Comments | Target group: Master students |
| Weblink | Course website |
| Admission requirements | Basic knowledge and skills regarding pattern recognition, numerical analysis, and statistics |
| Course application | Übung: https://courses.cs.unibas.ch |
| Language of instruction | English |
| Use of digital media | Online, optional |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Tuesday | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| wöchentlich | Wednesday | 14.15-16.00 | Alte Universität, Hörsaal -101 |
Dates
| Date | Time | Room |
|---|---|---|
| Tuesday 17.02.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 18.02.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 24.02.2026 | 10.15-12.00 | Fasnachtsferien |
| Wednesday 25.02.2026 | 14.15-16.00 | Fasnachtsferien |
| Tuesday 03.03.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 04.03.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 10.03.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 11.03.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 17.03.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 18.03.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 24.03.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 25.03.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 31.03.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 01.04.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 07.04.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 08.04.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 14.04.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 15.04.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 21.04.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 22.04.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 28.04.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 29.04.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 05.05.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 06.05.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 12.05.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 13.05.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 19.05.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 20.05.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 26.05.2026 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 27.05.2026 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) General Electives in Business and Economics: Additional Courses (Master's Studies: Business and Economics) Kernfächer und Seminar (Master's Studies: Computational Biology and Bioinformatics) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Concepts of Machine Intelligence (Master's Studies: Computer Science) Module: Concepts of Machine Intelligence (Master's degree subject: Computer Science) Module: Interdisciplinary and Transfer of Knowledge (Master's Studies: Actuarial Science) Module: Machine Learning Foundations (Master's Studies: Data Science) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) Specialization Module: Areas of Specialization in International and/or Monetary Economics (Master's Studies: International and Monetary Economics) |
| Assessment format | continuous assessment |
| Assessment details | Written exam Expected Date: TBA Admission to the examination: handing in "reasonable" solutions to >70% of the exercises Composition of the grade: examination result |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,5 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Informatik |