The course introduces the key algorithms and theory that forms the core of machine learning. It covers Major Approaches such as supervised, unsupervised, semi-supervised, and reinforcement learning. Topics covered include regression, decision trees, suport vector machines, artificial neural networks, Bayesian techniques, Hidden Markov, etc.
This course has the following prerequisites (none of them are university courses):
- Probability Theory
- Decision Theory
- Information Theory
- Linear Algebra
- Optimization & Search
| Title | Author(s) | Edition |
|---|---|---|
| Machine Learning | Tom M. Mitchell | 1st (1997) |
| Pattern Recognition & Machine Learning | Christopher M. Bhisop | 1st (2006) |
| Machine Learning – An Algorithmic Perspective | Marsland Stephen | 2nd (2015) |
| Introduction to Machine Learning | Alpaydin Ethem | 3rd (2014) |
| Machine Learning Yearning | Andrew Ng | Draft (2018) |
| Deep Learning | Ian Goodfellow, Yoshua Bengio, and Aaron Courville | 1st (2016) |
| The Manga Guide to Linear Algebra | Takashi & Inoue | 1st (2012) |
| Essentials of Statistics | Mario Triola | 5th (2015) |