Autumn 2023
Prof. Asim Tewari
Author: Mayank Bajaj
Pre-requisite courses: None
Pre-requisite skills: None
Course Content in Brief:
- An Introduction to Machine Learning with emphasis on data interpretation.
- The basics of statistical data interpretation and data handling is done for most of the course and towards the end Machine Learning concepts like SVM, etc. are introduced.
Motivation behind taking this course: A great course to take to get an introduction to Machine Learning. Specially if someone has done a basic data analysis course like AE102 then this is good continuation into Machine Learning. Also it is a basket course for CMInDS minor.
Info about Projects/Assignments: Every lecture there is a small quiz mostly covering what was taught in class which amount to 30% of the grades. The project is a group project where the professor assigns the group of 4-5 students. The project requires a little bit of collaborative effort and hence may seem difficult. The project is a good way to learn machine learning hands-on but also requires the members to put in extra effort to learn these skill outside of the lecture hours.
Evaluation Structure:
- 30% - Formative assessments (Small quizzes every lecture)
- 20% - Group Project
- 20% - Midsem
- 30% - Endsem
Attendance Policy: 100% mandatory attendance policy as said by sir with a promise of DX below 80% attendance. But professor is chill so no DX given but still wants students to attend.
Difficult level of Projects/Assignments: 3/5
Quizzes/midsem/endsem difficult level on a scale 1-5: 3/5
Level of effort you put into the course: 3/5
Feedback on Lectures (in terms of lecture delivery or ease of understanding, prof’s teaching style): The professor teaches with a lot of enthusiasm and attending lectures while making notes is essential to getting through the course. The lectures are interesting, but one has to make notes during class because the slides provided are a little vague and the class notes are of immense value because the exams are open handwritten notes only.
References OR online resources: Google Drive Link
General Fundae: Going to the lecture and making notes will surely get one a good grade. The key is to be consistent in going to classes as almost all the grade depends on it. The formative assessments are directly based on your attendance in class. The midsem and endsem too have questions which are easy to answer if you have written down all the notes properly. Project is something that will enhance your hands-on skill and also improve your grade.
Who can take this course?: Anyone interested in doing CMInDS minor can take up this course and no prerequisites are needed so it is a great introductory course for Machine Learning.