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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)

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