Guest Lecture on Statistical Foundations for Machine Learning

 

Report on Guest Lecture on Statistical Foundations for Machine Learning

Venue: MCA I
Date: 04/02/2026
Time: 2:30 PM to 3:30 PM
Resource Person: Dr. Sunil Job K. A. 

The PG Department of Computer Applications organized a guest lecture for the First Year MCA students as part of the Machine Learning course on 4 February 2026, from 2:30 PM to 3:30 PM. The session was handled by Dr. Sunil Job K. A., Chief of Academics at IPSR Solutions Limited, who is an experienced and expert resource person in the field.

The session focused on the statistical foundations that are essential for building machine learning models. Dr. Sunil Job began the class by explaining basic statistical concepts such as variables and their types, emphasizing their importance in data analysis and machine learning.

He then introduced the concept of correlation and explained how relationships between variables can be measured and interpreted. This was followed by a detailed discussion on regression analysis. Different types of regression techniques including Linear Regression, Multiple Regression, and Polynomial Regression were clearly explained with suitable real-world examples.

To make the session more practical and engaging, the resource person demonstrated the use of Microsoft Excel for statistical analysis. Students were guided to plot graphs, analyze data, and visually understand the behavior of variables and regression models. Hands-on practice helped students gain confidence in applying theoretical concepts.

The class was highly interactive, and Dr. Sunil Job K. A. explained complex statistical concepts in a simple and easy-to-understand manner. Real-world examples were used extensively, which helped students clearly understand the relevance of statistics in machine learning model building.

Overall, the session was informative and enriching. It provided students with a strong foundation in statistical concepts that are crucial for machine learning. The guest lecture was highly beneficial and enhanced the students’ understanding of data analysis and model development.