- Homeworks could be completed on Google Colab or locally on your machine via installing Anaconda
- Lecture: Thursday (13h-15h)
- Location: 5.56
Personal Q&A
My room is in 6.102 you can come to me with questions if needed. If you feel that you are stuck or need help with something (guidance, not pair programming) feel free to reach out to us. For additional requests email us.
Project suggestions
Please try to select a project based on your personal interest! Each project will have 3 slots which will be handed out on a ‘first come, first served’ basis! -> email: ffbence@student.elte.hu
Contacts
Balázs Pál: masterdesky@gmail.com | Task 1,2
Ágnes Becsei: agnes.becsei@ttk.elte.hu | Task 3
Norbert Deutsch: norbert.deutsch@ttk.elte.hu | Task 4,5
Ágoston Hunya: hunyaagoston@student.elte.hu | Task 6
Dániel Pozsár: danielpozsar@student.elte.hu | Task 7,8
Zoltán Kovács: k.ztoli17@gmail.com | Task 9, 10
This is an updated list for the second project.
Personal project suggestions are welcome:
Lecture materials:
- 01 Course introduction UPDATED: 2023. 09. 15.
- 02 Unsupervised & clustering UPDATED: 2023. 09. 20.
- 03 Supervised learning UPDATED: 2023. 09. 26.
- 04 Linear regression UPDATED: 2023. 10. 04.
- 05 Linear methods for classification, model validation UPDATED: 2023. 10. 11.
- 06 Regularization, model selection UPDATED: 2023. 10. 11.
- 07 SVM UPDATED: 2023. 10. 25.
- 08 Tree models UPDATED: 2023. 10. 25.
- 09 Fully connected neural networks
- 10 Convolutional neural networks
- 11 More neural networks
- 12 Simple OCR end-to-end example UPDATED: 2023. 11. 29. -> the whole github repository \w everything attached also for lecture 13!
- 13 Word embedding, RNNs - example1, example2
Lab materials
| Number (#) | misc. | homework | info | solution |
|---|---|---|---|---|
| 01 | data | HW 1 | UPDATED: 2023. 09. 20. | solution |
| 02 | dataset | HW 2 | UPDATED: 2023. 09. 20. | solution |
| 03 | dataset | HW 3 | UPDATED: 2023. 10. 04. | solution |
| 04 | dataset | HW 4 | UPDATED: 2023. 10. 11. | solution |
| 05 | dataset | HW 5 | UPDATED: 2023. 10. 11. | solution |
| 06 | code example | HW 6 | UPDATED: 2023. 10. 11. | solution |
| 07 | dataset, code example | HW 7 | UPDATED: 2023. 10. 25. | solution |
| 08 | dataset | HW 8 | N/A | solution |
| 09 | code example, dataset | HW 9 | UPDATED: 2023. 11. 06. | solution |
| 10 | Kaggle dataset | HW 10 | UPDATED: 2023. 11. 15. | solution |
| 11 | - | HW 11 | UPDATED: 2023. 11. 21. | Fine tuning |
| 12 | for extra points, GloVe file | HW 12 | UPDATED: 2023. 12. 06. | HW12 |
Requirements
Grading:
Based on two project, due dates TBD. Mid-semester project reports, Quarter-semester progress reports. Final scores: QS-PR_1 (4) + PR_1 (21) + QS-PR_2 (4) + PR_2 (21) = 50
| Mark | Interval |
|---|---|
| 5 | 42- |
| 4 | 35-42 |
| 3 | 28-35 |
| 2 | 21-28 |
| 1 | -21 |
Deadlines:
First project progress report deadline: 2024. 10. 4. 23:59 - short email describing what you have achived thus far and zipped project files (without data if large) First project deadline: 2024. 11. 02. 23:59 - (end of fall break)
Examplary reports of the first projects, these are some to strive for:
- SDSS best overall
- POLLUTION very good and informative EDA
- POLLUTION very good overall work
- SPOTIFY very good overall work
Second progress report deadline: 2024. 11. 15. 23:59 - same as previous Second project deadline: 2024. 12. 14. 23:59 - (end of semester)
Send your homework to the teacher who is responsible for your project! Please make the title of your mail your Neptun-code.
Projects will be provided OR custom projects that fit your interest are welcome! You need to analyze, come up with ideas how and what to model on the data. Perform meaningful supervised learning task and unsupervised exploration. Write an informative PDF report!