akjayant / Machine-Learning-Study-Material Public

Contains Stanford University (CS 229) Machine Learning study material

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
0 Linear Algebra review.pdf		0 Linear Algebra review.pdf
0 Probability Review.pdf		0 Probability Review.pdf
1 Linear Regression and Logistic Regression.pdf		1 Linear Regression and Logistic Regression.pdf
10 Principal Components Analysis.pdf		10 Principal Components Analysis.pdf
11 Independent Components Analysis.pdf		11 Independent Components Analysis.pdf
12 Reinforcement Learning.pdf		12 Reinforcement Learning.pdf
2 Generative Algorithms.pdf		2 Generative Algorithms.pdf
3 SVM.pdf		3 SVM.pdf
4 Bias Variance (Learning Theory) .pdf		4 Bias Variance (Learning Theory) .pdf
5 Regularization and Model Selection .pdf		5 Regularization and Model Selection .pdf
6 Online Learning and the Perceptron Algorithm. (optional reading) .pdf		6 Online Learning and the Perceptron Algorithm. (optional reading) .pdf
7 A Unsupervised Learning, k-means clustering.pdf		7 A Unsupervised Learning, k-means clustering.pdf
7 B Mixture of Gaussians .pdf		7 B Mixture of Gaussians .pdf
8 The EM Algorithm .pdf		8 The EM Algorithm .pdf
9 Factor Analysis.pdf		9 Factor Analysis.pdf
README.md		README.md