Search engines. Navigation systems. Game-playing robots. Learn how smart machines got that way in this course taught by a pioneer researcher in machine learning.
Overview
Syllabus
- By This Professor
- 01: Telling the Computer What We Want
- 02: Starting with Python Notebooks and Colab
- 03: Decision Trees for Logical Rules
- 04: Neural Networks for Perceptual Rules
- 05: Opening the Black Box of a Neural Network
- 06: Bayesian Models for Probability Prediction
- 07: Genetic Algorithms for Evolved Rules
- 08: Nearest Neighbors for Using Similarity
- 09: The Fundamental Pitfall of Overfitting
- 10: Pitfalls in Applying Machine Learning
- 11: Clustering and Semi-Supervised Learning
- 12: Recommendations with Three Types of Learning
- 13: Games with Reinforcement Learning
- 14: Deep Learning for Computer Vision
- 15: Getting a Deep Learner Back on Track
- 16: Text Categorization with Words as Vectors
- 17: Deep Networks That Output Language
- 18: Making Stylistic Images with Deep Networks
- 19: Making Photorealistic Images with GANs
- 20: Deep Learning for Speech Recognition
- 21: Inverse Reinforcement Learning from People
- 22: Causal Inference Comes to Machine Learning
- 23: The Unexpected Power of Over-Parameterization
- 24: Protecting Privacy within Machine Learning
- 25: Mastering the Machine Learning Process
Taught by
Michael L. Littman, PhD
