courses updated

stuff/2024_PSP.md

@@ -50,7 +50,8 @@ There will be theory classes as well as coding assignments or projects.
 2. Mid-sem Exam – 20%
 3. End-sem Exam - 30%
 
-Minimum attendance of 80% is needed to pass the course. 
+- Quizzes will follow the best (N-1) out of N policy.
+- Minimum attendance of 80% is needed to pass the course. 
 
 ### Plagiarism Penalty:<br>
 As heavy as possible. Zero-tolerance policy.

stuff/2025_ML2.md

@@ -0,0 +1,84 @@
+# EE698R: Advanced Topics in Machine Learning (Spring 2024)
+with focus on Generative AI and Trustworthy AI  
+
+**Units:** 3-0-0-0-9 (3 hours lecture; total 9 credits) <br>
+**Class timings:** MW 14:00-15:10 <br>
+**Instructor:** Vipul Arora <br>
+**Office hours:** After the class on Monday and Wednesday <br>
+
+## For Registration
+<!-- - Closed for UG students, still open for PG students
+- For auditing the course, please email [email protected] -->
+
+- I am planning to have around 50 UGs and rest all PGs. 
+- No limit on the number of PGs.
+- For UGs:
+  - First come first serve.
+  - Please do not email me. Apply via Pingala. 
+
+<!-- ### TAs
+
+| Name 	| E-mail 	|
+|---	|---	|
+| Sumit Kumar  	| [email protected] 	|
+| Rahul Kodag 	| [email protected] 	|
+| Parampreet Singh 	| [email protected] 	|
+| Akanksha singh 	| [email protected] 	|
+| Pratikhya Ranjit 	| [email protected] 	|
+| Adhiraj Banerjee 	| [email protected] 	|
+| Patnana Venkataramana Manikanta 	| [email protected] 	| -->
+
+
+## Course Objectives:
+This course aims at introducing the students to advanced topics in machine learning (ML). 
+The main focus will be on Generative machine learning and Trustworthy AI.
+The lectures will focus on mathematical principles, and there will be coding based assignments/project for implementation. 
+
+## Pre-requisites:
+- Basic course on machine learning (EE698V, EE603A or equivalent). [Lecture videos](https://www.youtube.com/playlist?list=PLbtAaXHMto-sQHH1qrYn8_D9Fze_D1KhE)
+- Basics of Programming (ESc101, EE698K or equivalent)
+
+The course will need a strong background in linear algebra and probability theory.
+
+## Topics:
+
+- Basics
+  - Probability theory 
+  - Neural Networks 
+- Generative Machine Learning
+	- Random sampling (Monte Carlo methods)
+	- Variational Inference
+	- Generative Adversarial Networks
+	- Normalizing Flows
+  - Diffusion Models
+- Trustworthy AI
+  - Uncertainty Estimation
+  - Model calibration
+  - Human in the loop learning (mislabeling detection, active learning)
+- Optional
+  - Explainable AI (LIME, SHAP, Grad-CAM)
+  - Data efficient machine learning (small data, model adaptation, semi-supervised learning)
+
+## Grading Scheme
+- Quizzes/Assignments - 30% 
+- Mid-semester Exam – 30%
+- End-semester - 40% 
+
+Minimum attendance of 80% is needed to pass the course.
+
+### Plagiarism Penalty:<br>
+As heavy as possible. Zero-tolerance policy.
+
+## References:
+  This course will take excerpts from some standard books on machine
+  learning and signal processing. But it will largely be based on
+  articles and research papers in ML and SP conferences (e.g.,
+  NeurIPS, ICML, ICLR, Interspeech, ICASSP, etc.) and journals (e.g., IEEE
+  TASLP, JMLR, IEEE PAMI, etc.). 
+
+Books:
+
+  - ["Pattern Recognition and Machine Learning", C.M. Bishop, 2nd Edition, Springer, 2011.](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
+  - ["Bayesian reasoning and machine learning", D. Barber, Cambridge University Press, 2012.](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf)
+  - ["Deep Learning", I. Goodfellow, Y, Bengio, A. Courville, MIT Press, 2016.](https://www.deeplearningbook.org/)
+