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
- 2025‑09-26: Assignment #1 is due on Thursday, 9th October, 5 PM.
- 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 | -- | -- |
| 7 | Linear Models | -- | PLA | -- | -- |
📝 Assignments
- Assignment #1 is due on Thursday, 9th October, 5 PM. Please read the instructions carefully—the required links are embedded within the PDF. Make sure to explore all available resources and troubleshoot thoroughly before reaching out to the instructor or TA. Assignment#1– Boilerplate Code
- Python is the default programming languages for the course. You should use it for programming your assignments unless otherwise explicitly allowed.
- Submit via Moodle, Google Form 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)