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| Semester | spring semester 2024 |
| 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 |
| Learning objectives | Understand the theoretical foundations of Machine Learning Understand and apply practical learning algorithms: linear and generalized linear models for regression and classification, neural networks, Support Vector machines & kernel methods, mixture models & clustering. 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 |
| Date | Time | Room |
|---|---|---|
| Tuesday 27.02.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 28.02.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 05.03.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 06.03.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 12.03.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 13.03.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 19.03.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 20.03.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 26.03.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 27.03.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 02.04.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 03.04.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 09.04.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 10.04.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 16.04.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 17.04.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 23.04.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 24.04.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 30.04.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 01.05.2024 | 14.15-16.00 | Tag der Arbeit |
| Tuesday 07.05.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 08.05.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 14.05.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 15.05.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 21.05.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 22.05.2024 | 14.15-16.00 | Alte Universität, Hörsaal -101 |
| Tuesday 28.05.2024 | 10.15-12.00 | Physik, Neuer Hörsaal 1, Foyer EG |
| Wednesday 29.05.2024 | 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) Modul: Concepts of Machine Intelligence (Master's degree subject: Computer Science) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Concepts of Machine Intelligence (Master's Studies: Computer Science) Module: Interdisciplinary and Transfer of Knowledge (Master's Studies: Actuarial Science) Module: Machine Learning Foundations (Master's Studies: Data Science) |
| Assessment format | continuous assessment |
| Assessment details | Oral exam Expected Date: 17/18/19 June 2023 (TBA), Spiegelgasse 1, room 00.003. 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 |