IMW - Mathematical Foundations of Artificial Intelligence and Machine Learning (2023)

Speakers and Syllabus

Eligibility
The course is meant for students, teachers, and professionals who are interested in the conceptual foundations of AI/ML. Applicants must have an undergraduate degree in science, mathematics, or engineering. The laboratory will require programming in Python (a few tens of lines of code per exercise). Registrants must either have experience in programming or be willing to learn it independently (some basic tutorials will be provided).

Day 1

• Lecture 1: Linear methods for classification

• Lecture 2: Linear methods for regression

• Lab: Perceptron

• Lecture 3: Overview of AI/ML

 Day 2

• Lecture 1: Neural Networks: Representation

• Lecture 2: Neural Networks: Training

• Lab: Backpropagation

• Lecture 3: To be decided

 Day 3

• Lecture 1: Support Vector Machines, Decision Trees, Nearest Neighbour Methods

• Lecture 2: Clustering

• Lab: K-means clustering

• Lecture 3: To be decided

Day 4

• Lecture 1: Uninformed search

• Lecture 2: Informed search

• Lab: Urban Navigation

• Lecture 3: Invited talk – Preethi Jyothi

Day 5

• Lecture 1: On-line learning

• Lecture 2: Knowledge representation and reasoning

• Lab: Bandits

• Concluding session

Instructor
  Shivaram Kalyanakrishnan

Teaching Assistants
    • Shreya Ilindra
    • Palti Ramya
    • Shabnam Sahay

Support
    • NCM Office: office.ncm@iitb.ac.in, +91 22 2576 5451.

Note: Participants must bring their own laptops, which can connect to the Internet through WiFi. No special software is required to be installed.

Time Table

The course will be held February 20–24 (Monday–Friday), with the following daily schedule.

    • Lectures will be held in SIC-201 KReSIT (see cell D3, building 18 in this map).
    • Labs will be held in the adjoining New CSE Building.
    • Tea and lunch will be provided in the third floor lobby, KReSIT.