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Introduction to Machine Learning

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Overview

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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.

Syllabus

  • 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
  • By This Professor

Taught by

Michael L. Littman, PhD

Reviews

4.1 rating at The Great Courses Plus based on 34 ratings

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