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66937-01 - Lecture with practical courses: Foundations of Deep Learning (6 CP)

Semester fall semester 2023
Course frequency Irregular
Lecturers Aurelien Lucchi (aurelien.lucchi@unibas.ch, Assessor)
Content The class focuses on the theoretical concepts behind Deep learning. We will discuss the following concepts:

General introduction to linear networks, activations, etc
Approximation Theory
Complexity Theory
Network Architectures
Optimization
Optimization Landscape of Neural Networks
Neural Tangent Kernel
Regularization
Generalization bounds
Adversarial examples
Learning objectives The main goal is to understand the theoretical foundations of Deep Learning.
This includes the following important concepts:
- Universal approximation: can a neural network approximate any arbitrary function?
- Optimization: how do we optimize the parameters of a neural network? what theoretical guarantees do we have about finding a good solution?
- Generalization: under what conditions does the solution of a neural network generalizes to unseen data?
- Adversarial robustness: how robust is a neural network to adversarial attacks?
Comments Exercise sessions will start the second week of the semester.

 

Admission requirements - Machine Learning (classification, regression, kernels, etc)
- Linear algebra
- Calculus, Basic concepts in topology
- Probability theory (random variable, expectation, density, etc)
- Some non-mandatory exercises will require coding in python (reasonable coding skills in another programming language should be sufficient to learn python)

Note that a significant part of the class focuses on understanding theoretical aspects, we will thus be covering proofs that require a good knowledge of the mathematical concepts discussed above.
Language of instruction English
Use of digital media No specific media used

 

Interval Weekday Time Room
wöchentlich Monday 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
wöchentlich Thursday 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001

Dates

Date Time Room
Thursday 21.09.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 25.09.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 28.09.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 02.10.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 05.10.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 09.10.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 12.10.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 16.10.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 19.10.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 23.10.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 26.10.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 30.10.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 02.11.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 06.11.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 09.11.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 13.11.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 16.11.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 20.11.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 23.11.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 27.11.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 30.11.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 04.12.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 07.12.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 11.12.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 14.12.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Monday 18.12.2023 10.15-12.00 Bernoullistrasse 30/32, Hörsaal 103
Thursday 21.12.2023 16.15-18.00 Spiegelgasse 5, Seminarraum 05.001
Modules Doctorate Computer Science: Recommendations (PhD subject: Computer Science)
Module: Applications of Distributed Systems (Master's Studies: Computer Science)
Module: Applications of Machine Intelligence (Master's Studies: Computer Science)
Module: Concepts of Machine Intelligence (Master's Studies: Computer Science)
Module: Electives in Data Science (Master's Studies: Data Science)
Module: Machine Learning Foundations (Master's Studies: Data Science)
Module: Methods of Machine Intelligence (Master's Studies: Computer Science)
Assessment format continuous assessment
Assessment details Continuous assessment

Note the following split:
30% homework
30% project (writeup and presentation)
40% written exam

A 50% score on HW sets is required to participate in the final exam.

Expected date: Thursday, February 1, 2024, 10-12 a.m.

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

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