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Andrew Ng - Stanford University
Lecture 1 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Definition of Machine Learning - 32:54
Unsupervised Learning - 50:14
Cocktail Party Problem - 56:43
Reinforcement Learning - 01:02:28
Lecture 2 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Linear Regression - 09:14
Parameters of a Learning Algor... - 20:32
Search Algorithms - 25:03
Gradient Descent - 26:16
Stochastic Gradient Descent - 45:51
Lecture 3 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Parametric Learning Algorithms - 09:53
Non-parametric Learning Algori... - 10:39
LOESS - 11:53
Locally-weighted Regression - 11:53
Justification for Least-square... - 29:44
Binary Classification - 50:01
Lecture 4 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Logistic Regression Model - 01:40
Exponential Family of Distribu... - 20:20
GLMs - 38:37
Generalized Linear Models - 38:37
Multinomial - 52:09
Lecture 6 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Naive Bayes - 01:13
Nonlinear Classifiers - 25:00
Neural Networks - 29:02
Choosing Linear Separators - 49:15
Geometric Margins - 49:16
Maximizing Margins - 49:16
Support Vector Machines - 50:39
Lecture 7 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Support Vector Machines - 00:28
Lagrange Multipliers - 23:27
SVM Dual Problem - 34:38
Karush-Kuhn-Tucker Conditions - 44:55
KKT Conditions - 44:55
Lecture 8 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Support Vector Machines - 00:27
Kernels - 03:57
Soft Margin SVM - 35:43
Coordinate Assent Algorithm - 45:57
Lecture 9 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Bias-Variance Tradeoff - 03:50
Empirical Risk Minimization - 28:05
Uniform Convergence Bound - 45:52
Error Bound - 51:01
Lecture 10 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
VC Dimension - 13:45
Model Selection - 36:42
Hold-Out Cross Validation - 41:25
K-fold Cross Validation - 44:37
Feature Selection - 54:21
Lecture 11 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Bayesian Statistics and Regula... - 00:56
Online Learning - 17:27
Bias-Variance Tradeoff - 31:53
Lecture 13 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Unsupervised Learning - 01:02
Mixture of Gaussians - 10:34
Text Clustering - 20:41
Mixture of Naive Bayes - 20:41
Factor Analysis - 31:21
Lecture 12 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Unsupervised Learning - 00:24
K-Means Clustering - 04:55
Density Estimation - 18:17
EM Algorithm - 30:30
Jensen's Inequality - 43:07
Lecture 14 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Factor Analysis - 00:27
Dimensionality Reduction - 37:37
Principal Component Analysis (... - 37:37
PCA Applications - 01:03:53
Lecture 15 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Principal Component Analysis (... - 00:26
Latent Semantic Indexing (LSI) - 05:04
Singular Value Decomposition (... - 18:00
Independent Component Analysis... - 39:48
Lecture 5 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Generative Learning Algorithms - 04:02
Gaussian Discriminant Analysis - 06:41
Naive Bayes - 44:32
Lecture 16 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Reinforcement Learning - 01:34
Markov Decision Process (MDP) - 09:01
Optimal Value Function - 44:12
Value Iteration - 50:11
Policy Iteration - 59:16
Lecture 17 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Reinforcement Learning - 01:09
Markov Decision Process (MDP) - 01:53
Policy Iteration - 08:12
Continuous State MDPs - 18:41
Value Iteration - 49:49
Lecture 18 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
State-Action Rewards - 04:12
Finite Horizon MDPs - 10:02
Linear Quadratic Regulation (LQR) - 28:31
Linearizing a Non-Linear Model - 41:12
Lecture 19 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
Debugging Reinforcement Learni... - 01:16
Differential Dynamic Programming - 35:03
Kalman Filters - 46:48
Linear Quadratic Gaussians (LQGs) - 46:48
Lecture 20 | Machine Learning (Stanford)
Topic:
Artificial Intelligence and Machine Learning
Teacher:
Andrew Ng
Concepts In This Lecture:
POMDPs - 04:04
Policy Search Algorithms - 06:20
Effectiveness of Policy Search - 42:21
Pegasus Algorithm - 47:51