CS-C3240: Machine Learning
| Course name | Machine Learning |
|---|---|
| Course code | CS-C3240 |
| Abbreviation | ML |
| Period | I |
| Lecturer | Francesco Croce, Azade Farshad, and Pekka Marttinen |
Description
Machine Learning is a Python-based programming course introducing fundamental machine learning methods, including regression, clustering, neural networks, and large language models. Assessment is primarily based on a project and a final exam.
Course material
Official material
Lecture notes are provided for the two weekly lectures. Weekly Jupyter notebook exercises reinforce the material, and project instructions include links to suitable datasets.
Extra material
Any introductory machine learning textbook is suitable supplementary reading. The 3Blue1Brown video series on neural networks is also an excellent resource.
Contents and workload
Overall workload
The course is worth 5 ECTS. Typical workload is around 7–8 hours per week, with the project deadline creating the busiest period.
Weekly contents
| Week | Topics |
|---|---|
| 1 | Course introduction, regression models |
| 2 | Classification, feature learning, visualization |
| 3 | Non-parametric methods, deep learning, project topic deadline |
| 4 | Clustering, probability theory |
| 5 | Reinforcement learning, language models |
| 6 | Project support, final project submission |
Practicalities
Assessment consists of automatically graded weekly programming exercises, a peer-reviewed project, and a mandatory final exam. Exercise sessions are available for support. The weighting of exercises, project, and exam may vary between course implementations.
Related courses
Official prerequisites
- MS-A0111: Differential and Integral Calculus 1
- MS-A0011: Matrix Algebra
- MS-A050X: First Course in Probability and Statistics
- CS-A1110: Programming 1
- CS-A1120: Programming 2
Additional prerequisites
MS-E1600 Probability Theory is useful for students wanting a deeper theoretical understanding but is not required.
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