1 - Introduction

2 - Introduction to Probability & Statistics

3 - Classifiers based on Bayes Decision Theory

4 - Parametric density-based Classifiers

5 - Gaussian-density based classification

6 - Non-parametric density estimation

7 - Linear Discriminative Classifiers

8 – Non-linear classifiers

9 - Evaluation

10 – Ethics in Machine Learning

11 – Unsupervised Learning

12 – Introduction to Deep Learning