Instructor

Prof. Asim Tewari

Semester

Autumn 2023-24

Course Difficulty

Moderate (3/5). Compared to other ML courses, this was easier to understand as the professor teaches well and wants you to learn rather memorising. Even the exams were simple and majorly based on past year papers. Also the exams were open book, so you were free to write anything you want and bring to exams. So if you have interest in the subject, this will be quite easy for you.

Time commitment needed

Not much really, classes are all you need to attend and make note (it helps at the end as exams are open book). No special efforts needed, if you attend lectures and note things down, its an easy BB+. In exams majorly time is spent on making notes. Prof even tells what he is going to ask so, attending lectures is all. He wants you to work on projects, but it’s a 4-5 mem team project, so you don�t need much effort, but you will learn a lot about ML.

Grading Statistics

Prof is lenient in taking exams, midsem and endsem are quite chill and based on past year papers. Grading overall is chill and TAs even check the paper leniently. Grading stats: AA 25 AB 56 AU 8 BB 45 BC 53 CC 34 CD 14 DD 8 FR 7 Total 250 So good grading if you put required efforts

Attendance Policy

It is not compulsory, but he takes quizzes each lecture, which at my time was 30% total, so about 1% each lecture. It is quite a lot, so everyone made sure to attend lectures. He takes a quiz on what is taught in that class.

Teaching Style

He used to teach from slides, and open for doubts, he is very good at explaining ML in a totally different perspective. He usually has 1.5 hrs lecture, teaches for 1hr 15 min and last 15 min are for quiz on what he taught in that lecture.

Feedback on Assignments/ Tutorials/ Projects

Projects were more on the corporate side; we were given to visualize our project in terms of selling it to someone or where it would be used. We were like the entrepreneurs for our product where our model is used. Different but a good way of looking at ML models from a corporate perspective. Projects were evaluated based on efforts; marks were given if efforts were put in. 3 reports were asked and 1 final reports. Every week, we were supposed to make a report on our progress based on the pattern given.

Feedback on Exams (Written Evaluation)

Exams were quite chill, as it was open book and if you have proper notes, and previous papers written down, it was just a copy paste test with some brain. Exams were moderate to easy and grading was done leniently by TAs. Paper was subjective, we were asked 40% theoretical and 60% application based, which had values changed from past years paper.

Future Tracks

This course can be a good start to your ML journey. You can take higher level courses after this.

Course Importance

It helped me figure out what I really like in ML, for me it was Computer Vision. It made me realise my passion in the field of AI/ML. It gives a brief overview of each topic in AI/ML, so it helps you realize your interests.

Additional Details

He did take an overview of deep learning, so I felt good. None of the basic-level courses required deep learning, but he taught very well.

Contact Details

Kush Patil - 8779631566