Course Objective

Course Information


# of Lectures Topics
1 Machine Learning/Pattern Recognition basics:
Supervised and Unsupervised Learning – Classification, Clustering and Regression, Parametric and Non-parametric Models, Curse of Dimensionality, Over-fitting, and Model Selection, Performance Measures
1 Data:  Attribute types, Basic Statistical description of Data, Review of probability theory
2 Bayesian Decision Theory: Likelihood Ratio Test, Bayes Risk; Bayes, ML and MAP Criteria, Naive Bayes classifier
1 Normal Variables and its Discriminant Analysis
1 Parametric Density Estimation: MLE, Bayesian Density Estimation
1 Nonparametric Density Estimation: Kernel Density Estimators and Nearest Neighbor Method
1 Regression: Linear Regression Analysis and Bayesian Linear Regression
2 Decision Trees and Random Forests, Ensemble Methods: Bagging and Boosting
4 Feature Selection and Extraction, Dimensionality Reduction : PCA and SVD
3 Linear Models for Classification: Fisher’s Linear Discriminant, Support Vector Machines
2 Introduction to Graphical Models: Bayesian Networks, Exact and Approximate Inference Methods
2 *Introduction to Reinforcement Learning: Policy gradient, Q-learning