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Yaser Abu-Mostafa - Caltech
Lecture 01 - The Learning Problem
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Types of Learning - 36:16
Supervised Learning - 38:52
Unsupervised Learning - 40:53
Reinforcement Learning - 45:28
Lecture 02 - Is Learning Feasible?
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Learning Feasibility - 08:02
Lecture 03 -The Linear Model I
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Linear Model - 05:50
Perceptron Learning Algorithm - 14:52
Pocket Algorithm - 18:25
Linear Regression - 27:32
Nonlinear Classifiers - 50:59
Nonlinear Transformation - 50:59
Lecture 04 - Error and Noise
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Nonlinear Transformation - 04:04
Error Measures - 14:22
Lecture 05 - Training Versus Testing
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Training vs Testing - 05:28
Dichotomies - 21:56
The Growth Function - 27:41
Break Points - 50:09
Lecture 06 - Theory of Generalization
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Hoeffding Inequality - 47:01
VC Inequality - 47:01
Lecture 07 - The VC Dimension
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
VC Inequality - 01:25
VC Dimension - 05:13
VC Dimension of Perceptrons - 16:07
Lecture 08 - Bias-Variance Tradeoff
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Approximation-Generalization T... - 05:43
Bias-Variance Tradeoff - 22:42
VC Analysis vs. Bias-Variance - 50:21
Lecture 09 - The Linear Model II
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Nonlinear Transformation - 07:27
Logistic Regression Model - 24:01
Gradient Descent - 52:32
Lecture 10 - Neural Networks
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Stochastic Gradient Descent - 05:01
Neural Networks - 20:04
Lecture 11 - Overfitting
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Overfitting - 05:26
Deterministic Noise - 42:31
Lecture 12 - Regularization
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Regularization - 05:35
Weight Decay - 37:30
Regularization - 55:51
Choosing a Regularizer - 55:51
Lecture 13 - Validation
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Validation vs. Regularization - 04:54
Validation - 04:54
Lecture 14 - Support Vector Machines
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Support Vector Machines - 04:42
Maximizing Margins - 05:33
Support Vectors - 50:05
Lecture 15 - Kernel Methods
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Support Vector Machines - 00:23
Kernels - 07:29
Kernel Trick - 15:51
Mercer's Condition - 42:33
Soft Margin SVM - 45:45
Lecture 16 - Radial Basis Functions
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Radial Basis Functions - 06:33
Nearest-Neighbor Method - 19:54
Lloyd's Algorithm - 32:02
Radial Basis Functions vs. Neu... - 48:41
Lecture 17 - Three Learning Principles
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa
Concepts In This Lecture:
Radial Basis Functions - 00:17
Occam's Razor - 06:24
Data Snooping - 45:05
Lecture 18 - Epilogue
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Yaser Abu-Mostafa