Timetable
| Lecture | Practice | Bonus homework | ||
|---|---|---|---|---|
| 11.02.2025 | Lecture 1: Introduction. (Lecture 1 slides) (Lecture 1 recording) | 12.02.2025 13.02.2025 | No practice sessions this week. We encourage you to start setting up your environment. Take a look at these sources and download Anaconda: Install Anaconda Python Numpy Tutorial IPython Tutorial | - |
| 18.02.2025 | Lecture 2: Probability and information theory. (Lecture 2 slides) (Lecture 2 recording) (Book chapter) | 19.02.2025 20.02.2025 | Practice 1: K-nearest neighbor classifier. (Background material) (Practice session recording) (Practice session slides) (Practice session Exercise) Homework 1 HW1 deadline : 25.02.2025 at 23:59. | - |
| 25.02.2025 | Lecture 3: Basics of Machine Learning. (Lecture 3 slides) (Lecture 3 recording) (Book chapter) | 26.02.2025 27.02.2025 | Practice 2: Implementing a softmax classifier. (Background material) (Practice 2 recording : Wednesday) (Practice 2 recording: Thursday) Practice session slides Homework 2 HW2 deadline : 04.03.2025 at 23:59. | - |
| 04.03.2025 | Lecture 4: Feed-forward networks. (Lecture 4 slides) (Lecture 4 recording) (Book chapter) | 05.03.2025 06.03.2025 | Practice 3 (part 1): Two-layer neural network. (Background material 1) (Background material 2) (Practice 3 (part 1) recording) Homework 3 (Practice session slides) HW3 deadline: 18.03.2025 at 23:59. | accuracy above 52% (max 5pts). |
| 11.03.2025 | Lecture 5: Back-propagation. (Lecture 5 slides) (Lecture 5 recording) (Book chapter) | 12.03.2025 13.03.2025 | Practice 3 (part 2): Two-layer neural network. (Background material 1) (Background material 2) (Practice 3 (part 2) recording) Practice session Exercise Quizz Homework 3 HW3 deadline: 18.03.2025 at 23:59. | accuracy above 52% (max 5pts). |
| 18.03.2025 | Lecture 6: Optimization and regularization. (Lecture 6 slides) (Lecture 6 recording) (Optimization book chapter) (Regularization book chapter) | 19.03.2025 20.03.2025 | Practice 4 (part 1): Fully-connected neural network. (Background material 1) (Background material 2) (Background material 3) (Practice 4 (part 1) recording) (Homework 4) HW4 deadline: 31.03.2025 at 23:59. | Separate notebook about dropout. |
| 25.03.2025 | Lecture 7: Convolutional neural networks. (Lecture 7 slides) (Lecture 7 recording) (Book chapter) (Lecture task) | 26.03.2025 27.03.2025 | Practice 4 (part 2): Fully-connected neural network. (Background material 1) (Background material 2) (Background material 3) (Practice 4 (part 2) recording) (Homework 4) HW4 deadline: 31.03.2025 at 23:59. | Separate notebook about dropout. |
| 01.04.2025 | Test 1! Computer room 2017 | 02.04.2025 03.04.2025 | Practice 5 (part 1): Convolutional networks. (Background material) (Practice 5 (part 1) recording) (Homework 5) HW5 deadline: 15.04.2025 at 23:59. | Separate notebook about batch normalization. |
| 08.04.2025 | Lecture 8: Sequential modeling: recurrent neural networks. (Lecture 8 slides) (Lecture 8 recording) (Book chapter) | 09.04.2025 10.04.2025 | Practice 5 (part 2): Convolutional networks. (Background material) (Practice 5 (part 2) recording) (Homework 5) HW5 deadline: 15.04.2025 at 23:59. | Separate notebook about batch normalization. |
| 15.04.2025 | Lecture 9: Autoencoders and GANs. (Lecture 9 slides) (Lecture 9 recording) (Autoencoders book chapter) (GANs book chapter) NB!: Test 1 Resit will be offered after the lecture (16:00). | 16.04.2025 17.04.2025 | Practice 6: Image classification using PyTorch. (Homework 6) (Practice 6 recording) HW6 deadline: 22.04.2025 at 23:59. | - |
| 22.04.2025 | Lecture 10: Deep reinforcement learning. (Lecture 10 code) (Lecture 10 recording) | 23.04.2025 24.04.2025 | Practice 7: Text classification using Pytorch. (Homework 7) (Practice 7 recording) HW7 deadline: 29.04.2025 at 23:59. | - |
| 29.04.2025 | Test 2! Computer room 2017 | 30.04.2025 01.05.2025 | Practice 8: HW7 discussion and announcements. (Practice 8 recording) | - |
| 06.05.2025 | Lecture 11: Software and Practical Methodology. (Lecture 11 recording) (Book chapter) | 07.05.2025 08.05.2025 | Practice 9: Wednesday - Project pitch (Track 3); Thursday - Challenges (Tracks 1 and 2). (Practice recording) | - |
| 13.05.2025 | Lecture 12: Attention and transformers. (Lecture 12 slides) (Lecture 12 recording) | 14.05.2025 15.05.2025 | Practice 10: Wednesday - Project Q&A (Track 3); Thursday - Challenges (Tracks 1 and 2). | - |
| 20.05.2025 | Lecture 13: Deep learning & the brain (by invited guest Jaan Aru). (Lecture 13 recording) | 21.05.2025 22.05.2025 | Practice 10: Wednesday - Project Q&A (Track 3); Thursday - Challenges (Tracks 1 and 2). | - |
| 27.05.2025 | Lecture 14: Applications (by an industry representative). (Lecture 14 slides) (Lecture 14 webcast) (Book chapter) | 27.05.2025 28.05.2025 | Practice 11: Wednesday - Project MVP Presentation (Track 3); Thursday - Challenges (Tracks 1 and 2). | - |
| 03.06.2025 | Test Resit will be offered at lecture time (14:15 in room 2017). | 04.06.2025 05.06.2025 | Practice 12: Wednesday - Project Q&A (Track 3); Thursday - Challenges (Tracks 1 and 2). | - |
| 10.06.2025 | - | 12.06.2025 | Practice 12: online - Project Final Presentation (Track 3); room 1022 - Challenges (Tracks 1 and 2). | - |
NB!: there will be no practice sessions in the first week, as shown in the table. They will start on week 2.