Machine Learning

Topics Covered in Class

1. Introduction to ML

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)

5. Bias and Variance

6. Introduction to Decision Trees

7. Random Forests

8. Clustering Algorithms

9. Principal Component Analysis

Assignment

Covid-19 Global Forecasting

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.