Instructor

Sharad Bhartiya

Semester

Autumn ‘21

Course Difficulty

This course serves as a introduction to machine learning using a lot of mathematics and proofs. It should be very easy for someone who has a basic understanding of Machine learning. If you are taking this course as a introduction to machine learning, then you will have to spend some time understanding the mathematics involved with every concept. Don’t go by the name of course, it has nothing to do with process engineering.

Time Commitment Required

There were only two exams - midsem and endsem. I prepared two days for each exam. Apart from that, not much time commitment other than classes and project work.

Grading Policy and Statistics

Sir initially suggested a absolute grading policy.
AA: 85% and above
AB: 75 - 85%
BB: 65 - 75 %
BC: 55 -65%
CC: 55 - 65%
CD 45 - 55%
DD: 35 - 45%
But at last relaxed the absolute values by 4-5 marks.

Attendance Policy

Not required

Pre-requisites

Basic understanding of Machine learning would help you easily get AB/AA

Evaluation Scheme

1 Midsem: 35%
1 Course Project: 20%
1 Endsem: 45%

Topics Covered in the Course

It covers most concepts of Machine learning and then moves on to basics of Deep learning. Professor follows Pattern Recognition book by Bishop for most of the course.

Teaching Style

Mechanism - Two 1.5 hour online lecture each week
Teaching Style - Sir teaches really well. He also answers all doubts of students and takes feedback of students to set his exams.

Tutorials/Assignments/Projects

Project will require a report and a presentation. No teams in project (single member project)

Feedback on Exams

Midsem - It was focused on mathematics and lots of calculation.
Endsem - after taking feedback from students about maths, sir made a easy concepts based endsem paper. High scoring paper.

Motivation for taking this course

I wanted to learn machine learning from a mathematical perspective and also for cminds elective.

How strongly would I recommend this course?

Very strongly. One of the best introduction to ML course.

When to take this course?

I took this in my 7th semester. Ideal semester can be 3rd/5th semester depending on your interest in ML.

Going Forward

After this, you should take a course on advanced machine learning either from IE/EE/CS department.

References Used

Pattern Recognition Book by Bishop

Review By: Anshul Gupta