Table of Contents

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Labs and Seminars

Two types of labs (tutorials) will be proposed for the course (alternating):

Teachers:

Schedule

(contents will be updated)

Date Topic Teacher Reading
20.02.2025 Lab 1: Preparations, Double Descent AS, VS NNTD lecture 1 (except 2.3, 5)
27.02.2025 Seminar 1 AS, JS
06.03.2025 Lab 2: Backpropagation, Computational Graph VS, JS
13.03.2025 Seminar 2 JS
20.03.2025 Lab 3: From Scratch: Initialization & regularization VS
27.03.2025 Seminar 3 JS
03.04.2025 Lab 4: CNN Fine-Tuning, Visualization & Adversarial Patterns JS
10.04.2025 Seminar 4 AS
17.04.2025 Lab 5: Metric Learning PS
24.04.2025 Lab 6: VAEs JS
01.05.2025 — public holiday —
06.05.2025 (Tuesday!) Seminar 5 AS
08.05.2025 — public holiday —
15.05.2025 Lab 7: Graph Neural Networks VS
22.05.2025 Seminar 6 AS

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

You may choose from the following submission variants:

  1. Report in pdf and Python source code
  2. Annotated Jupyter notebook with inline results and Python source code if applicable

Please do not submit data and any other redundant files. Sharing code that is not a required part of the assignment is permitted, for example additional visualization code or test cases for debugging.