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 |