Introduction to Machine Learning

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.

• 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