Machine Learning

Course Description

This course (CS229) -- taught by Professor Andrew Ng -- provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Machine Learning
Snapshot from Lecture 2, where Gradient Descent and other topics are covered.
5 ratings

Video Lectures & Study Materials

# Lecture Play Lecture
1 Introduction: Applications of Machine Learning, Logistics of the Class, Learning Theory (1:08:40) Play Video
2 Linear Regression, Gradient Descent, Normal Equations (1:16:16) Play Video
3 Locally Weighted Regression, Probabilistic Interpretation, Logistic Regression (1:13:14) Play Video
4 Newton's Method, Exponential Family, Generalized Linear Models (GLMs) (1:13:07) Play Video
5 Learning Algorithms: Generative, Gaussian Discriminant Analysis, Digression (1:15:31) Play Video
6 Neural networks, Naive Bayes, Support Vector Machines (1:13:09) Play Video
7 Optimal Margin Classifier, Primal/Dual Optimization (KKT), SVM Dual, Kernels (1:15:45) Play Video
8 Support Vector Machines: Kernels, Soft Margin, SMO Algorithm (1:17:19) Play Video
9 Learning Theory I: Bias/Variance, ERM, Union Bound, Hoeffding Inequality, Uniform Convergence (1:14:19) Play Video
10 Learning Theory II: Uniform Convergence, VC Dimension, Model Selection (1:12:56) Play Video
11 Bayesian Statistics and Regularization, Online Learning, Machine Learning Algorithms (1:22:19) Play Video
12 Unsupervised Learning, Clustering, Mixtures of Gaussians, Jensen's Inequality, EM Algorithm (1:14:23) Play Video
13 Expectation Maximization: Mixture of Gaussians, Naive Bayes, Factor Analysis (1:14:57) Play Video
14 The Factor Analysis EM steps, Principal Component Analysis (PCA) (1:20:40) Play Video
15 PCA: Latent Semantic Indexing, Singular Value Decomposition, Independent Component Analysis (1:17:18) Play Video
16 Reinforcement Learning I: MDP, Value Function, Value Interation, Policy Iteration (1:13:06) Play Video
17 Reinforcement Learning II: Continuous State MDP, Discretization, Models/Simulators, Fitted Value (1:17:00) Play Video
18 State-action Rewards, Finite Horizon MDPs, Dynamical Systems: Models, LQR, Riccati Equation Play Video
19 Debugging RL Algorithm, Linear Quadratic Regulation, Kalmer Filters, Linear Quadratic Gaussian (1:15:55) Play Video
20 POMDPs, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning (1:16:40) Play Video

Comments

Displaying 4 comments:

sathyabhama wrote 8 years ago.
Let me know more details about SVM. kindly suggest some
books ro read for beginners


rsztom wrote 8 years ago.
chinese student of academy

xfeng wrote 9 years ago.
instreating ML.

kyi phyu wrote 9 years ago.
how are you?

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