This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, Linear regression, Logistic regression, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, K-mean algorithm). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you'll also learn how to apply learning algorithms.

Course Objectives 

The goal of this course is to introduce the students to the concept of modern Machine Learning approaches. The main objectives of this course are-

• To be introduced with the machine learning techniques and applications.
• To understand the basic machine learning theories (supervised and unsupervised).
• To demonstrate the machine learning techniques with the programming language. 
• To be able to apply the techniques in real-life data and environments. 

Course Learning Outcomes (CLO)

By the end of the semester, students should be able to:

1. CLO1: relate the machine learning applications to the real world and surrounding environments. 
2. CLO2: understand the supervised and unsupervised learning methods in terms of Machine Learning.
3. CLO3: implement the machine learning techniques (supervised and unsupervised) with python libraries. 
4. CLO4: present and defend technical aspects of modern Machine Learning approaches.