Course Contents:
The goal of the course is to acquaint students with the basic Machine Learning concepts and algorithms. Specifically, the course will cover parametric and non-parametric density estimation, linear and non-linear classification, unsupervised learning including clustering and dimensionality reduction, performance evaluation of predictive algorithms, ethical issues in machine learning.

Study Goals:
After succesfully completing this course, the student is able to:

• explain the basic concepts and algorithms of machine learning and underlying statistical concepts.
• implement, apply and evaluate ML algorithms in Python
• explain the concept of and identify (implicit) bias in data and ML algorithms

Expected prior knowledge:

• CSE1100/TI1206 Object-oriented programming
• CSE1305/TI1316 Algorithms and Data Structures
• CSE1200/TI1106M Calculus
• CSE1205/TI1206M Linear Algebra
• CSE1210/TI2216M Probability Theory and Statistics