Foundations of Machine Learning / July-Nov 2025
Updates
- Sep 01: New Lecture is up: (foml-13) Probabilistic Generative Models
- Aug 25: New Lecture is up: (foml-12) Decision Theory [slides] [annotated-slides]
- Aug 25: New Lecture is up: (foml-11) Bayesian Regression [slides] [annotated-slides]
- Aug 21: New Lecture is up: (foml-10) Bias Variance Decomposition [slides] [annotated-slides]
- Aug 14: New Lecture is up: (foml-09) Regularized Least Sqaures [slides] [annotated-slides]
- Aug 13: New Lecture is up: (foml-08) SGD
- Aug 13: New Lecture is up: (foml-07) Geometric Interpretation for Least Squares [slides] [annotated-slides]
Course Description
Machine Learning has lately become the driving force behind numerous high-performing AI products deployed in real-world across diverse disciplines. Tech giants such as Google, Microsoft, Facebook, Amazon, etc. have strongly been employing the Machine Learning workforce in the past few years for developing various applications in Computer Vision, Natural Processing, etc. Hence, it has recently become one of the most sought-after learning courses.
(AI2000/AI5000) Foundations of Machine Learning Course Contents
Welcome to the Foundations on Machine Learning (FoML) course! In this course, you will explore the foundational concepts and techniques behind machine learning, a key driver of modern artificial intelligence. We will discuss key concepts in supervised and unsupervised learning, probability theory, and neural networks, with a strong emphasis on the probabilistic and Bayesian approaches prevalent in modern machine learning. The course integrates mathematical concpets with hands-on programming experience, covering topics like linear models, kernel methods, and clustering techniques. By the end of the semester, students will be equipped with a deep understanding of machine learning principles and practical skills for data analysis and modeling.
Logistics
Class Room: LHC-13
Timings: Slot-C
Visit this page regularly for the updates and information regarding the course.
Instructors
Teaching Assistants
Rishik Vempati
Sunayna Padhye
Naveen George