Stanford CS229 - Machine Learning - Andrew Ng
Andrew Ng

folder AndrewNg-MachineLearning-CS229-Stanford (20 files)
fileLecture 1 _ Machine Learning (Stanford)-UzxYlbK2c7E.mp4 176.61MB
fileLecture 2 _ Machine Learning (Stanford)-5u4G23_OohI.mp4 206.41MB
fileLecture 3 _ Machine Learning (Stanford)-HZ4cvaztQEs.mp4 200.53MB
fileLecture 4 _ Machine Learning (Stanford)-nLKOQfKLUks.mp4 199.61MB
fileLecture 5 _ Machine Learning (Stanford)-qRJ3GKMOFrE.mp4 206.06MB
fileLecture 6 _ Machine Learning (Stanford)-qyyJKd-zXRE.mp4 200.49MB
fileLecture 7 _ Machine Learning (Stanford)-s8B4A5ubw6c.mp4 206.21MB
fileLecture 8 _ Machine Learning (Stanford)-bUv9bfMPMb4.mp4 211.07MB
fileLecture 9 _ Machine Learning (Stanford)-tojaGtMPo5U.mp4 202.39MB
fileLecture 10 _ Machine Learning (Stanford)-0kWZoyNRxTY.mp4 199.62MB
fileLecture 11 _ Machine Learning (Stanford)-sQ8T9b-uGVE.mp4 227.12MB
fileLecture 12 _ Machine Learning (Stanford)-ZZGTuAkF-Hw.mp4 201.60MB
fileLecture 13 _ Machine Learning (Stanford)-LBtuYU-HfUg.mp4 204.81MB
fileLecture 14 _ Machine Learning (Stanford)-ey2PE5xi9-A.mp4 219.80MB
fileLecture 15 _ Machine Learning (Stanford)-QGd06MTRMHs.mp4 211.74MB
fileLecture 16 _ Machine Learning (Stanford)-RtxI449ZjSc.mp4 201.22MB
fileLecture 17 _ Machine Learning (Stanford)-LKdFTsM3hl4.mp4 211.58MB
fileLecture 18 _ Machine Learning (Stanford)--ff6l5D8-j8.mp4 211.18MB
fileLecture 19 _ Machine Learning (Stanford)-UFH5ibWnA7g.mp4 301.36MB
fileLecture 20 _ Machine Learning (Stanford)-yCqPMD6coO8.mp4 211.97MB
Type: Course
Tags: machine learning, statistics, Regression

title= {Stanford CS229 - Machine Learning - Andrew 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.

# Prerequisites

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= {}

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