Machine Learning
Overview
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.
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Prerequisites
This course has the following prerequisites (none of them are university courses):
- Probability Theory
- Decision Theory
- Information Theory
- Linear Algebra
- Optimization & Search
Textbooks
| 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) |