Fundamentals of Machine Learning

Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works.

In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning. Finally, it addresses when it's appropriate to use (and not use) machine learning in problem solving, as well as an example of scientific research incorporating machine learning principles.

Students of all levels should be able to follow this reasonably-paced introduction to one of the most important engineering breakthroughs of our time.

 

1. Types of Machine Learning
2. A Geometric View of Supervised Learning
3. Generalization Performance
4. Artificial Intelligence and Board Games
5. Go as a Supervised Learning Problem
6. Fundamentals of Game-Playing Systems
7. Building a Go Machine Learning Program
8. Introduction to Neural Networks
9. Why do Deep Neural Networks Succeed?
10. The Mystery of Deep Learning
11. Some Caveats of Using Machine Learning
12. Advanced Concepts: Stacked Monte Carlo
13. Homework