Homeworks of the Machine Learning course, which involve several fields of Supervised Learning, and also some topics of Unsupervised Learning:
- Homework 1: Associative Learning: Decision Trees
- Homework 2: Bayesian and Lazy Learning
- Homework 3: Regression and Neural Networks
- Homework 4: Clustering (K-Means and EM - Estimation Maximization Algorithm)
Each of this homeworks is divided in 2 parts, a Pen-And-Paper part, which focuses on a theoretical problem-solving approach which can be found the solution in G075.pdf and Programming and critical analysis part, which the solution you can find in G075.ipynb.
You can also find each Homework assignment in the respective Homework directory.
| Homework | Grade(0/20) |
|---|---|
| Homework 1 | 20 |
| Homework 2 | 18.5 |
| Homework 3 | 20 |
| Homework 4 | 20 |