Instructor

Alankar Alankar

Section

S1

Semester

4th

Course Difficulty

The course was moderately difficult.

Time Commitment Required

6 hrs/week

Grading Policy and Statistics

Grading was quite lenient. Following was the grading scheme: •Quizzes: 8 marks •Assignments (computer programs in python): 12 marks •Mid-sem: 20 marks •End-sem: 45 marks •Final project demonstration: 15 marks

Attendance Policy

Attendance for lectures was optional, but attempting quizzes and tutorials in class was mandatory; otherwise, they were considered void.

Pre-requisites

None

Topics Covered in the Course

The course equips us with essential skills to analyze large datasets and make data-driven decisions. It covers key algorithms and techniques in supervised, unsupervised, and reinforcement learning. This knowledge is useful in fields like mechanical engineering for optimizing simulations, predictive modeling, and process improvements. Additionally, the course prepares us for advanced studies and research in data science and AI.

Teaching Style

The professor first covered all the relevant theory and then dedicated one lecture slot to solving tutorials in class, which were graded. Additionally, assignments and quizzes were conducted to ensure we could apply what we learned.

Tutorials/Assignments/Projects

The assignments and tutorials were generally easy and didn’t take much time. However, the project was more challenging. We had the freedom to choose any topic, and the instructor also provided a list of suggestions. The only requirement was that the topic had to combine ML and Mechanical Engineering. These assignments and projects were crucial for understanding the concepts and their applications.

Course Importance

This course builds up a foundation of DS and ML concepts on which more advanced and specialized topics can be explored. These may include NLP, Computer Vision, Reinforcement Learning, etc.

Review By: Ashlesha Shelke