Stanford CS229 - Machine Learning - Ng
Andrew Ng

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AndrewNg-MachineLearning-CS229-Stanford (20 files)
Lecture 1 _ Machine Learning (Stanford)-UzxYlbK2c7E.mp4176.61MB
Lecture 2 _ Machine Learning (Stanford)-5u4G23_OohI.mp4206.41MB
Lecture 3 _ Machine Learning (Stanford)-HZ4cvaztQEs.mp4200.53MB
Lecture 4 _ Machine Learning (Stanford)-nLKOQfKLUks.mp4199.61MB
Lecture 5 _ Machine Learning (Stanford)-qRJ3GKMOFrE.mp4206.06MB
Lecture 6 _ Machine Learning (Stanford)-qyyJKd-zXRE.mp4200.49MB
Lecture 7 _ Machine Learning (Stanford)-s8B4A5ubw6c.mp4206.21MB
Lecture 8 _ Machine Learning (Stanford)-bUv9bfMPMb4.mp4211.07MB
Lecture 9 _ Machine Learning (Stanford)-tojaGtMPo5U.mp4202.39MB
Lecture 10 _ Machine Learning (Stanford)-0kWZoyNRxTY.mp4199.62MB
Lecture 11 _ Machine Learning (Stanford)-sQ8T9b-uGVE.mp4227.12MB
Lecture 12 _ Machine Learning (Stanford)-ZZGTuAkF-Hw.mp4201.60MB
Lecture 13 _ Machine Learning (Stanford)-LBtuYU-HfUg.mp4204.81MB
Lecture 14 _ Machine Learning (Stanford)-ey2PE5xi9-A.mp4219.80MB
Lecture 15 _ Machine Learning (Stanford)-QGd06MTRMHs.mp4211.74MB
Lecture 16 _ Machine Learning (Stanford)-RtxI449ZjSc.mp4201.22MB
Lecture 17 _ Machine Learning (Stanford)-LKdFTsM3hl4.mp4211.58MB
Lecture 18 _ Machine Learning (Stanford)--ff6l5D8-j8.mp4211.18MB
Lecture 19 _ Machine Learning (Stanford)-UFH5ibWnA7g.mp4301.36MB
Lecture 20 _ Machine Learning (Stanford)-yCqPMD6coO8.mp4211.97MB
Type: Course
Tags: machine learning, statistics, Regression

title= {Stanford CS229 - Machine Learning - Ng},
journal= {},
author= {Andrew Ng},
year= {2008},
url= {},
license= {},
abstract= {#Course Description

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.


Students are expected to have the following background:
Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
Familiarity with the basic probability theory. (CS109 or Stat116 is sufficient but not necessary.)
Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

Introduction (1 class)

* Basic concepts. 

Supervised learning. (7 classes)

* Supervised learning setup. LMS.
* Logistic regression. Perceptron. Exponential family. 
* Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes. 
* Support vector machines. 
* Model selection and feature selection. 
* Ensemble methods: Bagging, boosting. 
* Evaluating and debugging learning algorithms. 

Learning theory. (3 classes)

* Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds. 
* VC dimension. Worst case (online) learning. 
* Practical advice on how to use learning algorithms. 

Unsupervised learning. (5 classes)

* Clustering. K-means.
* EM. Mixture of Gaussians.
* Factor analysis.
* PCA (Principal components analysis).
* ICA (Independent components analysis). 

Reinforcement learning and control. (4 classes)

* MDPs. Bellman equations. 
* Value iteration and policy iteration. 
* Linear quadratic regulation (LQR). LQG. 
* Q-learning. Value function approximation. 
* Policy search. Reinforce. POMDPs. 
keywords= {machine learning, statistics, Regression},
terms= {},
superseded= {}