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Introduction to Machine Learning

Introduction to Machine Learning « Series from 2020

Series from 2020

Broadcast info
Genres:	Documentary

This series teaches you about machine-learning programs and how to write them in the Python programming language. For those new to Python, a “get-started” tutorial is included.

Professor Michael L.

Littman covers major concepts and techniques, all illustrated with real-world examples such as medical diagnosis, game-playing, spam filters, and media special effects.

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Introduction to Machine Learning: Telling the Computer What We Want

1

Telling the Computer What We Want

Introduction to Machine Learning: Starting with Python Notebooks and Colab

2

Starting with Python Notebooks and Colab

Introduction to Machine Learning: Decision Trees for Logical Rules

3

Decision Trees for Logical Rules

Introduction to Machine Learning: Neural Networks for Perceptual Rules

4

Neural Networks for Perceptual Rules

Introduction to Machine Learning: Opening the Black Box of a Neural Network

5

Opening the Black Box of a Neural Network

Introduction to Machine Learning: Bayesian Models for Probability Prediction

6

Bayesian Models for Probability Prediction

Introduction to Machine Learning: Genetic Algorithms for Evolved Rules

7

Genetic Algorithms for Evolved Rules

Introduction to Machine Learning: Nearest Neighbors for Using Similarity

8

Nearest Neighbors for Using Similarity

Introduction to Machine Learning: The Fundamental Pitfall of Overfitting

9

The Fundamental Pitfall of Overfitting

Introduction to Machine Learning: Pitfalls in Applying Machine Learning

10

Pitfalls in Applying Machine Learning

Introduction to Machine Learning: Clustering and Semi-Supervised Learning

11

Clustering and Semi-Supervised Learning

Introduction to Machine Learning: Recommendations with Three Types of Learning

12

Recommendations with Three Types of Learning

Introduction to Machine Learning: Games with Reinforcement Learning

13

Games with Reinforcement Learning

Introduction to Machine Learning: Deep Learning for Computer Vision

14

Deep Learning for Computer Vision

Introduction to Machine Learning: Getting a Deep Learner Back on Track

15

Getting a Deep Learner Back on Track

Introduction to Machine Learning: Text Categorization with Words as Vectors

16

Text Categorization with Words as Vectors

Introduction to Machine Learning: Deep Networks That Output Language

17

Deep Networks That Output Language

Introduction to Machine Learning: Making Stylistic Images with Deep Networks

18

Making Stylistic Images with Deep Networks

Introduction to Machine Learning: Making Photorealistic Images with GANs

19

Making Photorealistic Images with GANs

Introduction to Machine Learning: Deep Learning for Speech Recognition

20

Deep Learning for Speech Recognition

Introduction to Machine Learning: Inverse Reinforcement Learning from People

21

Inverse Reinforcement Learning from People

Introduction to Machine Learning: Causal Inference Comes to Machine Learning

22

Causal Inference Comes to Machine Learning

Introduction to Machine Learning: The Unexpected Power of Over-Parameterization

23

The Unexpected Power of Over-Parameterization

Introduction to Machine Learning: Protecting Privacy within Machine Learning

24

Protecting Privacy within Machine Learning

Introduction to Machine Learning: Mastering the Machine Learning Process

25

Mastering the Machine Learning Process

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