Università Roma Tre

1. Introduction to the Course
- Areas of Interest in Machine Learning.
- Potential of ML Models and Methods.

2. Regression
- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.

3. Classification
- Logistic Regression for Classification.
- Overfitting in Classification.
- Boosting: AdaBoost Algorithm.
- Naïve Bayes.
- Support Vector Machines.

4. Clustering
- k-means and k-means++ Algorithms
- Expectation Maximization.
- Hierarchical Clustering.

5. Artificial Neural Networks
- Architecture of Artificial Neural Networks.
- Backpropagation Learning Algorithm.
- Applications of Artificial Neural Networks.

Lecture slides.