Basic Information
- Course Code: EE769
- Course Name: Introduction to machine learning
- Course Offered In: 2023-2024
- Semester Season: Spring
- Instructors: Prof. Amit Sethi
- Prerequisites: Should know basics of python also should be able to work with new libraries like numpy, matplotlib, sk-learn.
- Difficulty (1 being easy and 5 being tough): 3
Course Content
Basic maths for ML, Linear regression, Logistic Regression, Support Vector Machines, neural networks, CNN, Transformers, LSTM, Monte carlo methods
Feedback on Lectures
The lectures are well organised like they start from very basic maths try to cover majority breadth for introduction to ML. The prof. always tries to provide a visual imagination of the concepts.
Feedback on Evaluations
The course has absolute grading, but it is not very hard to get 90+ marks if you do the assignments which are basic, the midsem and endsem are mostly subjective. Midsem exam A4 size cheat sheet was allowed and open notes for endsem. There is option for project as in you can either do ASSIGNMENT3 or Project but Assignment 3 has 15 marks where as project has 25. No marks for ATTENDENCE.
Study Material and Resources
Slides are enough, prof. also provides extra reading material.
Follow-up Courses
Advanced Machine learning
Final Takeaway
The course is good provides full breadth about ML, Maths is also not too much. It will be good for beginners as it provides you with python assignments which gives hands on feel for ML