Machine Learning
Topics Covered in Class
2. Bayes Decision Theory, Naive Bayes Classification
3. Maximum Likelihood Estimation (Discussed about how to estimate Gaussian distribution with unknown parameters)
4. Linear Regression (Scatter Plot, Adaptive Linear Filtering, Unconstrained Optimization Techniques: Gradient Descent, Newton's Method, Gauss Newton's Method)
6. Introduction to Decision Trees
9. Principal Component Analysis
Assignment
Sample Codes related to the topics discussed in class are attached below.
Recommended Books
1. Pattern Classification by Duda, Hart, Stork
2. Neural Networks-A Comprehensive Foundation by Simon Haykin
3. Pattern Recognition and Machine Learning by Bishop
Course Project
Click on this link to enter your group members' names and project title. Up to three members will be allowed in a group.