course taken on edx.
topics covered- Introduction to Machine Learning
- Linear Classifier and Perceptron
- Hinge loss, Margin boundaries and Regularization
- Project 1: Automatic Review Analyzer
- Linear Regression
- Nonlinear Classification
- Recommender Systems
- Project 2: Digit recognition(part 1)
- Introduction to Feedforward Neural Networks
- Feedforward Neural Networks, Back Propagation, and Stochastic Gradient Descent
- Recurrent Neural Networks
- Convolutional Neural Networks
- clustering
- generating models
- Em algorithm
- gaussian fixtures
- reinforcement leanring
- Natural langauge Processing