Here are complete notes from all lectures presented at the Machine learning for business and UAntwerp. The information from tutorial slides are already included in the lecture notes.
Lectures
- MLB 1. lecture - Intro to Machine Learning
- MLB 2. lecture - CRISP-DM
- MLB 3., 4. and 5. lecture - Classification
- MLB 5. lecture - How to choose the best model a visualize it’s performance
- MLB 6. lecture - Naive Bayes + Support Vector Machines + Random Trees
- MLB 7. lecture - Similarity, Neighbors and Clusters
- MLB 8. lecture - Text and Association rules Mining
- MLB 9. lecture - Introduction to Recommender Systems
- MLB 10. lecture -Neural networks and Deep learning
- MLB 11. lecture - Ensembles
- additional sources: MIT Intro to Deep Learning
Exam questions
Other materials (CZE only)
Disclaimer: many formulations in the notes are my “own explanations” which helped me to understand the topics. I don’t know, if those explanations are 100% correct, but at least they form a different view on the course material. Consulting official materials is recommended.
If you find something wrong, don’t hesitate to contact me - Disclaimer