Machine Learning, MDS Master, UPC

The official course homepage.

Theory slides:

0. Presentation slides
1. Intro to ML slides
2. Linear regression slides
   • polynomial regression notebook
   • notes on probability etc.
   • notes on the Gaussian distribution
3. Probabilistic clustering
   • code for clustering demos
4. Linear classification
• generative classifier demo
• perceptron demo
5. Neural Networks notes and slides
6. Nearest neighbor prediction
• nearest neighbor demo
7. Decision Trees
8. Random Forests
9. Support vector machines
10. Kernel methods

Lab sessions:

1. Pre-processing
2. SVD and applications
3. Linear regression
4. Resampling
5. Probabilistic clustering
6. Linear classification
7. Multilayer Perceptron
8. Random Forests