Spring 2020
Prof. Preethi Jyothi
Author: Miloni Atal
Pre-requisite courses: None
Pre-requisite skills: Programming(Python was preferred), familiarity with basic probability theory, linear algebra, multivariable calculus
Course Content:
- Basic foundations of ML, classification/regression, Naive Bayes’ classifier, linear and logistic regression
- Supervised learning: Decision trees, perceptron, support vector machines, neural networks.
- Unsupervised learning: k-means clustering, EM algorithm.
- Other topics: feature selection, dimensionality reduction, boosting, bagging.
- Brief introduction to ML applications in computer vision, speech and natural language processing.
Motivation to take up the course: Learn basic concepts of Machine Learning, currently being a very sought after field
Information about Projects/Assignments: The plan was to give us two Programming Assignments(20%) and one Project(10%). In programming assignments, we were given real datasets to apply the concepts learned in theory, these were hosted on Kaggle with bonus points for people with the top leaderboard score. The marking of the assignments was lenient. For the projects, we were asked to make a team of 2-3 students decide on a problem statement and deliver final presentation at end (this was scraped due to COVID situation)
Quizzes/Midsem/Endsem papers Difficulty: 4/5
Overall Course Difficulty: Moderate
Average Time Commitment: (Apart from lectures and tutorials) Between 3-6 hrs
Attendance Policy: 60% minimum attendance
General funda: Attend all the lectures to get a good understanding of the concepts and to not miss out on surprise in-class quizes.
Grading stats:
| AA | 16 |
| AB | 21 |
| BB | 7 |
| BC | 24 |
| CC | 6 |
| CD | 5 |
Professor’s Teaching Style: The Professor’s teaching style was really simple and the course was run at a moderate pace, attending the lectures led to a good understanding of the concepts. Added to that the programming assignments were a direct application of the theory and were fun to do(if you like programming). The exams were open book, very applicative and challenging. Tutorials were posted with solutions released within a few days, followed by doubt sessions conducted by the TAs.
Should you do this course?: Strongly recommend this to anyone interested in learning ML, and who loves programming