EEC 351: Fundamentals of AI/ML

Autumn 2026-27 · Department of Electrical Engineering, IIT Roorkee

Previous offering · Autumn 2025-26

Course information

SemesterAutumn 2026–27 InstructorParikshit Pareek · pareek [at] ee.iitr.ac.in LecturesMon & Tue · 16:05 – 17:00 @ GB005 · Fri 17:05 – 18:00 is a backup slot if needed Office hoursFri · 17:05 – 18:00 @ 214A, EE (instructor's office) PiazzaTBA Teaching assistantAyushi Jolotia · ayushi_j [at] ee.iitr.ac.in

Announcements

  • 2026‑07‑08 — First class is on Thursday, 16 July 2026.
  • 2026‑06‑18 — Course website for the Autumn 2026-27 offering is live. Announcements are posted here regularly; email is sent only when something is urgent.

Course objectives

  • Comprehend the historical evolution and foundational concepts of AI/ML.
  • Build mathematical intuition for machine-learning principles.
  • Explore core theoretical frameworks and evaluation strategies.

Course content

# Topic Slides Essential reading Additional Homework
The weekly schedule will appear here as the term begins. Material from the previous run is on the Autumn 2025-26 page.

Assignments

  • Assignments will be posted here as they are released. TBA.
  • Python is the default programming language for the course; use it unless a task explicitly allows otherwise.
  • Submit via Moodle, Google Form, or GitHub — as specified in each assignment.
  • Honor code: any copying earns a zero on the assignment; more severe penalties may follow.
  • Late submissions incur penalties as announced with each assignment.

References & resources

Recommended texts

  • Probabilistic Machine Learning: An Introduction — Kevin Murphy, MIT Press, 2022/2023.
  • Learning from Data: A Short Course — Yaser S. Abu-Mostafa, Malik Magdon-Ismail & Hsuan-Tien Lin, AMLBook, 2017.

Supplementary

  • Coursera ML (Andrew Ng)
  • Relevant paper links shared on Piazza

Grading policy

  • CWS — 30 marks: announced & surprise quizzes; assignments & peer discussions.
  • MTE — 30 marks: written exam (any format).
  • ETE — 40 marks: written exam (any format).

Exam papers