CEN/CSE 421: Machine Learning
Course Objective
We have entered the era of big data. This deluge of data calls for automated methods of data analysis, which is what machine learning or pattern recognition provides. Machine learning as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data!). This introductory pattern recognition/machine learning course will give an overview of many popular models and algorithms used in modern machine learning – both statistical and non-statistical. The course will give the student the basic ideas and intuition behind these methods, as well as a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning techniques and apply them on real life problems.
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 |
