Machine Learning

Course: Machine Learning

Course Content

• Introduction to Machine Learning

• Supervised Learning and Unsupervised Learning

• Reinforcement Learning

• Linear Regression & Multivariate Regression

• Partial Least Squares & Shrinkage Methods

• Linear Discriminant Analysis

• Linear Classification and Logistic Regression

• Project

• Perceptron Learning & Artificial Neural Networks

• Training and Validation

• Regression Trees & Decision Trees

• ROC Curve & Evaluation Measures

• Ensemble Methods

• Random Forest

• Gradient Boosting

• Naïve Bayes and Support Vector Machine

• Hidden Markov Models & Gaussian Mixture Models

• Clustering

• Expectation Maximization

• Project