Table of Contents

Labs and Seminars

Two types of practical classes will be proposed for the course (alternating):

Schedule

(contents will be updated)

Date Topic Teacher Reading
18.02.2026 Lab 1: Preparations, Double Descent + MLP AS, VS NNTD lecture 1 (except 2.3, 5)
25.02.2026 Seminar 1 JH
04.03.2026 Lab 2: CNN Finetuning & Visualization + AY
11.03.2026 Seminar 2 AS
18.03.2026 Lab 3: From Scratch: Initialization & regularization TA
25.03.2026 Seminar 3 JH
01.04.2026 Lab 4: Transformers BP
08.04.2026 Seminar 4 AS
15.04.2026 Lab 5: Metric Learning KZ/PS
22.04.2026 Seminar 5 JH
29.04.2026 Lab 6: Graph Neural Networks VS
06.05.2026 Seminar 6 AS
13.05.2026 — Rector's day —
20.05.2026 Lab7: TBA TA+BP

Seminars

The seminar assignments are published 1 week in advance before the seminar. You are expected to prepare for it at home. We discuss the problems and solutions in the class. You are not required to submit you solutions, but if you solved a problem you will be invited to present it in the class. Seminars are not scored by points but they are important for gaining technical understanding, which will be finally evaluated in the written exam.

Examples of problems with solutions: examples.pdf (to be updated)

Labs

The solutions of the practical labs have to be submitted using the upload system

Submission Regulations

We expect you to submit:

  1. Python source code without redundancies (e.g. series of failed attempts).
  2. Report in pdf / printed Jupyter notebook with inline results

Sharing code that is not a required part of the assignment is permitted, for example additional visualization code or test cases for debugging.