Courses

I am teaching six courses this semester at CMR Institute of Technology, including two theory courses and four lab sessions.  

BCS714A: Deep Learning

Semester: 7

This course introduces fundamental concepts and models such as convolutional and recurrent neural networks, focusing on their applications in image and language processing. It covers key techniques like regularization and optimization, paired with practical Python-based assignments to build hands-on skills for solving real-world problems. 

BAI701: Deep Learning and Reinforcement Learning Lab

Semester: 7

This course provides practical experience in implementing deep learning models such as CNNs, RNNs, autoencoders, and pretrained networks for real-world tasks. Students engage in hands-on experiments and projects to apply deep learning techniques, assessed through continuous evaluation and practical tests.

BAD702: Statistical Machine Learning Lab

Semester: 7

This course introduces essential statistical techniques for data science, including exploratory data analysis, sampling distributions, hypothesis testing, regression, and discriminant analysis. Through practical labs using real-world datasets, students gain hands-on experience in applying statistical methods for data interpretation, prediction, and decision-making. 

BAIL504: Data Visualization Lab

Semester: 5

This course provides practical experience in creating effective visual representations of data using various techniques and tools. Students engage in hands-on experiments to develop skills in visual perception and design data visuals that support business intelligence and decision-making, assessed through continuous evaluation and practical exams. 

BCS358C: Project Management with GIT Lab

Semester: 3

The course offers practical training in essential Git commands for repository management, branching, merging, and collaboration using remote repositories. Students gain hands-on experience with advanced Git operations, including tagging and history analysis, to effectively manage software projects. Assessment includes continuous evaluation of practical exercises, reports, and viva, ensuring comprehensive skill development in version control. 

1BAIA103: Introduction to AI and Applications 

Semester: 1

The course provides foundational knowledge in artificial intelligence, covering key concepts, historical background, machine learning techniques, AI applications, and emerging trends. Students learn about different AI types, algorithms, ethical considerations, and practical uses across various domains, along with hands-on activities for developing skills in AI tools and prompt engineering.