- Introduction to Machine Learning
- Linear Regression
- Classification Using Logistic Regression
- Overfitting and Regularization
- Feasibility of Learning
- Support Vector Machine
- Neural Network
- Decision Trees
- Unsupervised Learning
- Theory of Generalization
- Bias and Fairness in ML
