Coursera Machine Learning PPT & Note & Programming Exercise

Coursera Stanford Machine Learning - Andrew Ng

All Programming Exercise (also the ungraded exercise) were completed.

Note is from http://www.holehouse.org/mlclass/ , and it's download to ./Note/ directory.

Week 1

1. Introduction: PPT & Note
2. Linear Regression with One Variable: PPT & Note
3. Linear Algebra Review: PPT & Note

Week 2

1. Linear Regression with Multiple Variables: PPT & Note
2. Octave/Matlab Tutorial: PPT & Note

Programming Exercise (ex1): Linear Regression, Exercise PDF & Completed Code

Week 3

1. Logistic Regression: PPT & Note
2. Regularization: PPT & Note

Programming Exercise(ex2): Logistic Regression, Exercise PDF & Completed Code

Week 4

1. Neural Networks: Representation: PPT & Note

Programming Exercise (ex3): Multi-class Classification and Neural Networks, Exercise PDF & Completed Code

Week 5

1. Neural Networks: Learning: PPT & Note

Programming Exercise (ex4): Neural Networks Learning, Exercise PDF & Completed Code

Week 6

1. Advice for Applying Machine Learning: PPT & Note
2. Machine Learning System Design: PPT & Note

Programming Exercise (ex5): Regularized Linear Regression and Bias v.s. Variance, Exercise PDF & Completed Code

Week 7

1. Support Vector Machines: PPT & Note

Programming Exercise (ex6): Support Vector Machines, Exercise PDF & Completed Code

Week 8

1. Unsupervised Learning (Clustering) &: PPT & Note
2. Dimensionality Reduction: PPT & Note

Programming Exercise (ex7): K-means Clustering and Principal ComponentAnalysis, Exercise PDF & Completed Code

Week 9

1. Anomaly Detection: PPT & Note
2. Recommender Systems: PPT & Note

Programming Exercise (ex8): Anomaly Detection and RecommenderSystems, Exercise PDF & Completed Code

Week 10

1. Large Scale Machine Learning: PPT & Note
2. Dimensionality Reduction: PPT & Note

Week 11

1. Application Example: Photo OCR: PPT & Note
2. Conclusion: Note