IIT Madras BS Degree Diploma Level

The IIT Madras BS Degree Diploma Level provides a robust foundation in machine learning and business data management. This program is designed to equip students with essential skills and knowledge in these domains through a series of specialized courses and hands-on projects.

1. Machine Learning Foundations
2. Business Data Management
3. Machine Learning Techniques
4. Business Data Management - Project

1. Machine Learning Foundations

Instructor(s):

• Harish Guruprasad Ramaswamy: Assistant Professor at IIT Madras, specializing in machine learning, statistical learning theory, and optimization. Previously a research scientist at IBM and a post-doc at the University of Michigan.
• Arun Rajkumar: Assistant Professor at IIT Madras with expertise in machine learning and statistical learning theory, previously a research scientist at Xerox Research Center.
• Prashanth L.A.: Assistant Professor at IIT Madras with a background in machine learning and optimization, previously a postdoctoral researcher at the University of Maryland and INRIA Lille.

Key Concepts:

• Machine Learning Problem Identification: Recognize whether a problem can be framed as a machine learning issue.
• Decomposition of Problems: Break down machine learning problems into fundamental components using calculus, linear algebra, probability, and optimization.
• Supervised Learning and Linear Regression: Understand relationships between solving equations, projections, and linear least squares regression.
• Eigenvalues and Eigenvectors: Visualize these concepts as matrix properties useful in unsupervised learning applications like dimensionality reduction and image compression.
• Gradient Descent Methods: Implement, debug, and understand failure modes in gradient descent for unconstrained optimization problems.
• Gaussian Mixture Models: Use and interpret Gaussian mixture models for data, constructing algorithms for parameter learning.

Course Structure:

• 12 weeks of coursework with weekly assignments.
• Two in-person quizzes and one end-term exam.

2. Business Data Management

Instructor(s):

• Dr. G Venkatesh: Professor of Practice at IIT Madras, involved in education technology and data management projects.
• Prof. Suresh Babu: Professor at IIT Madras with expertise in applied macroeconomics, trade, development, and industrial economics.
• Dr. Milind Gandhe: Chief Program Officer at IIIT Bangalore with extensive experience in the corporate sector and projects in various verticals.

Key Concepts:

• Business Context: Understand consumption patterns, micro-economic concepts of demand and supply, and data usage in business operations.
• Firm-Level and Industry-Level Data Analysis: Analyze data from firms and industries, including performance metrics and strategic insights.
• Data Management: Techniques for structuring and representing business data, using worksheets for interpretation and presentation, and working with large datasets.

Course Structure:

• 12 weeks of coursework with weekly assignments.
• Two in-person quizzes and one end-term exam.

3. Machine Learning Techniques

Instructor:

• Arun Rajkumar: Assistant Professor at IIT Madras, specializing in machine learning algorithms, statistical learning theory, and applications in education and healthcare.

Key Concepts:

• Machine Learning Algorithms: Deep understanding of algorithms for regression, classification, and clustering, including model properties, objectives, optimization, and evaluation.
• Model Tuning: Techniques to address underfitting and overfitting in machine learning models.
• Algorithm Selection: Choosing appropriate algorithms based on problem requirements.
• Unsupervised Learning: Study of unsupervised learning techniques including PCA, Kernel PCA, K-means clustering, and Gaussian Mixture Models.

Course Structure:

• 12 weeks of coursework with weekly assignments.
• Two in-person quizzes and one end-term exam.

4. Business Data Management - Project

Instructor(s):

• Dr. Ashwin J. Baliga: Assistant Professor of Sales at IESEG School of Management with expertise in B2B marketing and research.
• Dr. Aaditya Chandel: Research Scientist at IIT Madras with a focus on mechanical engineering and startup ecosystem.
• Dr. G Venkatesh: Professor of Practice at IIT Madras with experience in education technology and data management.
• Dr. Milind Gandhe: Chief Program Officer at IIIT Bangalore with a strong corporate background and expertise in machine intelligence.

Project Overview:

• Problem Identification: Work with a business firm to identify data-related issues or problems.
• Data Collection and Analysis: Collect primary data, clean, analyze, and provide valuable insights.
• Report and Presentation: Document the entire project process and present findings professionally.

Course Structure:

• Independent research project with proposal, midterm submission, final report, and viva voce.

Future Courses

After completing these courses, you will also be taking:

• Machine Learning Practice (BSCS2008): Building on foundations and techniques learned, focusing on practical application.
• Machine Learning Practice - Project (BSCS2008P): Applied project work based on the Machine Learning Practice course.
• Business Analytics (BSMS2002): Further exploration of data analysis techniques within business contexts.

Diploma in Data Science: Upon completing these courses and projects, you will earn a diploma in data science from a renowned institution in India, emphasizing your comprehensive understanding of machine learning, business data management, and practical applications in the field.