This document provides an overview of machine learning concepts including linear classifiers, perceptron algorithm, kernels, support vector machines, neural networks, deep learning, clustering, dimensionality reduction, matrix factorization, collaborative filtering, mixture models, EM algorithm, naive Bayes algorithm, and their applications to tasks such as sentiment analysis, content recommendation, image/text annotation and translation. It also discusses online learning, overfitting, regularization, generalization, semi-supervised learning, and active learning.

1. Subjectmatter:
 Machine learning overview
 Features, feature vectors, linear classifiers
 Online learning
 Perceptron algorithm
 Application to sentiment analysis
 Non-linear classification and regression
 Kernels
 Passive aggressive algorithm
 Over fitting, regularization, generalization
 Content recommendation
 Support Vector Machines (SVM)
 Machine Learned Ranking (MLR)
 Cascades
 Boosting algorithm
 Neural Networks(NN)
 Deep learning
 Back-propagation algorithm
 Image/text annotation, translation
 Gradient descentalgorithm
 Unsupervised learning overview
 Clustering
 Dimensionality reduction
 Matrix factorization
 Collaborative filtering
 Generative models
 Mixture models
 EM algorithm
 Semi-supervised and active learning overview
 Naïve Bayes algorithm
 Tagging
 Information extraction