Program Overview

This introductory course provides an overview of basic concepts and techniques in machine learning. More broadly, it introduces the students to some broad classes of statistical inference problems, such as classification, clustering and regression. The course will cover some regression and classification techniques in detail – with formal understanding of how they work- while it will cover basic ideas and provide broad intuitions about some of the more sophisticated techniques such as Artificial Neural Networks. The course will use the Python programming language for hands-on work.

Course/Program Objectives

• To introduce main classes of statistical inference problems: regression, classification and clustering
• To provide a comprehensive overview of major classification techniques and develop an understanding of their relative merits and demerits
• To provide an overview of regression techniques
• To introduce unsupervised learning problems with a focus on clustering techniques

Course/Program Content

• Linear Regression: Simple linear regression, Multiple Linear Regression
• Classification models: Linear Classifiers, Logistic regression, Decision trees, Naive Bayes classifier, Maximum Likelihood Estimation, k-NN, Support Vector Machines
• Artificial Neural Networks: Perceptrons, Multi-layer networks, Hidden layers
• Unsupervised learning: k-means clustering

Program Pedagogy

The program will be conducted through a mix of lectures that explain conceptual ideas along with hands-on experiments using Python with sample datasets.

Ideal Participant Profile

Professionals who are familiar with Python or have previously attended the Basic Python MDP program.

Learning Outcomes

• Recognize and understand the main classes of machine learning problems.
• Apply appropriate methods for classification problems and analyze the results
• Understand basic clustering techniques
• Apply appropriate regression techniques based on the problem at hand

Faculty Profile

Prof. Kaushik Gopalan

Associate Professor – Computer Science

Prof. Kaushik Gopalan has a Ph.D. in Electrical Engineering from the University of Central Florida and joined FLAME University in 2020. He has taught varied Math and Computer Science courses at FLAME, including a very well-received course on introductory Python. Prior to joining FLAME, he served as a scientist at the Space Applications Centre – ISRO at Ahmedabad from 2011-2020. He was a Researcher with the Earth System Science Interdisciplinary Center, University of Maryland, College Park from 2008-2011. Prof. Kaushik’s research has involved applying statistical analysis and methods to solve a variety of problems in the field of Satellite Remote Sensing, with a focus on the retrieval of geophysical parameters from satellite data.

Cancellation

In the unforeseen event of Program Cancellation by the Centre, participants will be refunded the entire amount of Program Fees. If the cancellation is requested by the participant then the Program Fee will be withheld as credit for the next schedule of the same program or an alternative program that the candidate opts for.