EEC 351: Fundamentals of AI/ML
Course Information
- Semester: Autumn Semester 2025-2026
- Instructor: Parikshit Pareek (email: pareek AT ee.iitr.ac.in)
- Lectures: Thu • 03:00–03:55 PM; Fri • 4:05–5:00 PM; (Venue: GB205)
- Office Hours: Tue • 4:05–5:00 PM; (Venue: 214A, EE)
- Piazza: https://piazza.com/indian_institute_of_technology_roorkee/fall2025/eec351
- TA: Rajdeep R. Dwivedi (email: rajdeep_rd AT ece.iitr.ac.in)
📌 Announcements
- 2025‑08-22: Additional Class on 26th August, Wednesday.
- 2025‑07‑13: Course Announcements will be posted here regularly. Email notifications will only be sent if information is urgent.
- 2025‑07‑12: Course website launched.
🎯 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.
📅 Cource Content
| Index | Topic | Content | Essential Reading | Additional | Homework |
|---|---|---|---|---|---|
| 1 | Kick-off | Slides | -- | -- | HW |
| 2 | Introcuction & History | Slides | Trends in Compute | Watch "Imitation Game", Watch "AlphaGo" | -- |
| 3 | Linear Algebra Review | -- | Norm.pdf, Linear Algebra Review, Quadratic Functions | Matrix Calculus | HW |
| 4 | Probability Review (Self Study) | -- | Probability Review | -- | -- |
| 5 | Learning Theory | -- | Concentration Inequalities, Learning Problem, Learning Theory Notes | -- | -- |
| 6 | Energy & Loss Functions | -- | EBM Tutorial | -- | -- |
📝 Assignments
- Python is the default programming languages for the course. You should use it for programming your assignments unless otherwise explicitly allowed.
- Submit via Moodle or GitHub—- as specified in each assignment.
- Honor Code: Any cases of copying will be awarded a zero on the assignment. More severe penalties may follow.
- Late submissions will incur penalties, as annouced with 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 Resources:
- Coursera ML (Andrew Ng)
- Relevant paper links 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)