Instructor: Prof. M. Vlachos & Prof. Seyed Moosavi
Teaching Assistants: Stergios Konstantinidis & Donia Gasmi
This repo contains both non-graded lab material and graded coursework.
- Labs — Weekly Jupyter notebooks used in class for practice (not graded).
- Assignments — Individual programming assignments (graded).
- Project — Group project (graded).
Schedule may be adjusted slightly depending on progress.
| Week | Date | Topic | Notes |
|---|---|---|---|
| 1 | Sept 15 | Introduction / Data / Methods / Visualization | |
| 2 | Sept 22 | No Lecture – Lundi du Jeûne Fédéral | Quiz 1 due Sept 29 |
| 3 | Sept 29 | Linear Regression | Quiz 2 due Oct 6 |
| 4 | Oct 06 | Classification | Quiz 3 due Oct 13 |
| 5 | Oct 13 | Neural Networks | Quiz 4 due Oct 20 |
| 6 | Oct 20 | Text Analytics | Quiz 5 due Oct 27 |
| 7 | Oct 27 | Internet of Things | Quiz 6 due Nov 9 |
| 8 | Nov 03 | No Lecture – Block Week | |
| 9 | Nov 10 | Recommender Systems | Project released |
| 10 | Nov 17 | Generative AI | Project repo setup |
| 11 | Nov 24 | Dimensionality Reduction | Project stand-up 1 |
| 12 | Dec 01 | Clustering | Project stand-up 2 |
| 13 | Dec 08 | Interpretability for AI & Sustainable Development for AI | Project due Dec 15 |
| 14 | Dec 15 | Social Implications of AI + Project Presentations | Prep for exam |
Here are useful open datasets for assignments, projects, and practice.
- OpenML — large online platform with datasets for ML benchmarking.
- Kaggle Datasets — community-driven ML datasets.
- UCI Machine Learning Repository — classic curated datasets.
- FiveThirtyEight — data behind popular data-journalism articles.
- Google Dataset Search — meta-search engine for datasets.
- AWS Open Data Registry — cloud-hosted scientific & public datasets.
- Microsoft Research Open Data — curated academic datasets.
- World Bank Open Data — global development indicators.
- Data.gov & Data.gov.uk — open government datasets.
- DataHub — catalog of community datasets.
- Reddit r/datasets — user-shared datasets across domains.
- 75 Public Datasets for Machine Learning — curated list with explanations.
-
Regression
- Appliance Energy Prediction — predict energy usage.
- Facebook Comment Volume — predict comment volume on posts.
-
Recommendation Systems
- MovieLens — classic benchmark dataset for recommender systems.
- Book-Crossings — large dataset of book ratings.