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| Semester | spring semester 2021 |
| 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, mandatory |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Tuesday | 10.15-12.00 | - Online Präsenz - |
| wöchentlich | Wednesday | 14.15-16.00 | - Online Präsenz - |
| Date | Time | Room |
|---|---|---|
| Tuesday 02.03.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 03.03.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 09.03.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 10.03.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 16.03.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 17.03.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 23.03.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 24.03.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 30.03.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 31.03.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 06.04.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 07.04.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 13.04.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 14.04.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 20.04.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 21.04.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 27.04.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 28.04.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 04.05.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 05.05.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 11.05.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 12.05.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 18.05.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 19.05.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 25.05.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 26.05.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Tuesday 01.06.2021 | 10.15-12.00 | - Online Präsenz -, -- |
| Wednesday 02.06.2021 | 14.15-16.00 | - Online Präsenz -, -- |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) 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) |
| Assessment format | continuous assessment |
| Assessment details | Oral exam Expected Date: 23/24/25 June 2021, online Admission to the examination: handing in "reasonable" solutions to >70% of the exercises |
| 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 |