ML.txt

Machine Learning
Schedule:
Week 1:

Introduction
Linear regression with one variable
Linear Algebra review (Optional)
Week 2:

Linear regression with multiple variables
Octave tutorial
Programming Exercise 1: Linear Regression
Week 3:

Logistic regression
Regularization
Programming Exercise 2: Logistic Regression
Week 4:

Neural Networks: Representation
Programming Exercise 3: Multi-class Classification and Neural Networks
Week 5:

Neural Networks: Learning
Programming Exercise 4: Neural Networks Learning
Week 6:

Advice for applying machine learning
Machine learning system design
Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance
Week 7:

Support vector machines
Programming Exercise 6: Support Vector Machines
Week 8:

Clustering
Dimensionality reduction
Programming Exercise 7: K-means Clustering and Principal Component Analysis
Week 9:

Anomaly Detection
Recommender Systems
Programming Exercise 8: Anomaly Detection and Recommender Systems
Week 10:

Large scale machine learning
Application example: Photo OCR


https://github.com/phyous/machine-learning-coursera/blob/master/lectures/Lecture11.pdf


In taking this class you’ll build basic models for Regression, Neural Networks, Support 
Vector Machines, Clustering, Recommendation Systems, and Anomaly Detection. 

Octave  - Free version of MATLAB

Matrix Multiplication v Element Wise Multiplication

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Octave: Maximum Values with Matrices (Exercise C.4) 

Octave 
Octave: Maximum Values with Matrices 
http://youtu.be/b_2G1ouSEZ0

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Supervised v Unsupervised Learning

F-scores Accuracy Precision and Recall


11 - Spam Classification Example
Error Analysis
Precision and Recall


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Ex1

https://skydrive.live.com/redir?resid=8060571A0FCD32D!175&authkey=!ALDz5RoDeKbkXaY

Logistic Function 

https://skydrive.live.com/redir?resid=8060571A0FCD32D!164

Bias Variance TradeOFf

https://skydrive.live.com/redir?resid=8060571A0FCD32D!170