Machine Learning and Neural Networks 2021/2022

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbor and provide the basic concepts of Artificial Neural Networks (ANN) and We will also cover the basic clustering algorithms. In this course also we will discuss various issues related to the application of machine learning algorithms. We will also cover the basic clustering algorithms. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.


Course Objectives

• To introduce the most common methods for machine learning

• To study the basics of supervised and unsupervised learning

• To introduce the basics of Artificial Neural Network

Expected Outcome

After completing this course, the students will be able to:

i) Differentiate various learning approaches, and to interpret the concepts of supervised learning

ii) Identify and apply appropriate supervised learning algorithm for a given problem

iii) Apply theoretical foundations of decision trees to identify best split to label data points

vi) Illustrate and apply clustering algorithms and identify its applicability in real life problems