added PSP course

index.md

@@ -21,7 +21,7 @@ I work on developing learning based methods mostly for audio processing (music,
 
 I received my B.Tech. and Ph.D. degrees in [<ins>Electrical Engineering</ins>](http://www.iitk.ac.in/ee/) from the [<ins>Indian Institute of Technology (IIT) Kanpur</ins>](http://www.iitk.ac.in/), India. My Ph.D. thesis was titled "[<ins>Analysis of Pitched Polyphonic Music for Source Transcription</ins>](https://drive.google.com/file/d/0By8wZfM49Y2ScC1vc2lVX0I1c1U/view)", where I worked on analyzing music audio to identify and transcribe different instruments/voices playing simultaneously. During postdoc at Oxford University (UK), I developed [<ins>speech recognition</ins>](https://www.youtube.com/watch?v=Tgr3Y_U9BsQ) systems using linguistic principles, with applications in automatic language teacher and speech recognition for low-resource languages. At Amazon in Boston (USA), I worked on audio classification for developing Alexa [<ins>home security</ins>](https://www.theverge.com/2018/9/20/17883428/amazon-alexa-guard-alarm-ring-smart-home-security-price) system, with research focusing on classification with imbalanced data.
 
-### Research interests:
+### Current Research interests:
 Speech, Music, Audio processing
 
 Machine learning:

stuff/2024_PSP.md

@@ -0,0 +1,65 @@
+# EE698K: Programming for Signal Processing (Fall 2024)
+
+**Units:** 3-0-0-0-9 (modular course; 3 hours lecture; total 9 credits) <br>
+**Class timings:** MTh 12:00-13:15 (venue TBD) <br>
+**Instructor:** Vipul Arora <br>
+**Office hours:** After each class <br>
+
+## Registration Note: 
+- It is a PG-only course, open to all EE PGs only.
+
+<!-- ## TAs
+
+| Name 	| E-mail 	|
+|---	|---	|
+| Kunal Aggarwal 	| [email protected] 	|
+| Deepender Kumar 	| [email protected] 	| -->
+
+## Course Objectives:
+Most of the research in signal processing is heavily computational.
+Good programming skills are indispensable for good computational research.
+The knowledge of data structures and algorithms is necessary for writing efficient and easy to understand codes. 
+With advacing computational technologies and infrastructure, it is even more important to know these concepts well for effective and efficient use.
+The course will discuss basics of programming and basics of digital signal processing.
+
+This course is tailored specifically for EE PGs with limited programming experience. 
+We will use Python to implement the concepts we learn in this course. (Python is one of the most popular high-level languages highly recommended for researchers).
+There will be theory classes as well as coding assignments.
+
+## Pre-requisites:
+- None
+
+## Lecture Plan
+
+| Topics                                                                                                       | No of weeks |
+|--------------------------------------------------------------------------------------------------------------|-------------|
+| Python basics                                                                                                | 1           |
+| Abstract Data Types, Arrays                                                                                  | 1           |
+| Linked Lists, Stacks and Queues                                                                              | 2           |
+| Trees and Binary Search Tree                                                                                 | 1           |
+| Heaps, Sets, Hash Tables                                                                                     | 1           |
+| Graphs and Dynamic Programming                                                                               | 1           |
+| Linear Time Invariant Systems, Fourier Transforms                                                            | 2           |
+| Z-transforms, symbolic programming, Linear Constant  Coefficient Difference Equations, Digital Filter Design | 2           |
+| Applications in audio processing                                                                             | 2           |
+
+## Grading Scheme
+1. Quizzes and assignments – 50%
+2. Mid-sem Exam – 20%
+3. End-sem Exam - 30%
+
+### Plagiarism Penalty:<br>
+As heavy as possible. Zero-tolerance policy.
+
+## References:
+- This course will take excerpts from some standard books on data structures and algorithms, and digital signal processing.
+- Textbook for signal processing: "Digital Signal Processing: A Computer-Based Approach" by Sanjit K. Mitra
+- See my introduction to basics of Python [here](https://www.youtube.com/playlist?list=PLbtAaXHMto-vV3G334P1iuj_4P_-qyT3x).
+
+Books:
+- “Data Structures and Algorithms” by A. V. Aho, J. E. Hopcroft, J. D. Ullman​
+- https://livebook.manning.com/book/grokking-algorithms/table-of-contents ​
+- Discrete time signal processing (3rd ed.) – by Oppenheim and Schafer
+- Digital Signal Processing (4th ed.) – by Proakis and Manolakis
+- The Scientist and Engineer’s Guide to Digital Signal Processing - by Steven W. Smith (Available online https://www.dspguide.com/pdfbook.htm)
+- Think DSP: Digital Signal Processing in Python - by Allen B. Downey (Available online https://greenteapress.com/wp/think-dsp/)

teaching.md

@@ -5,11 +5,13 @@ slug: /teaching
 ---
 ## Courses
 
-* **2024 Spring**: <span style="color: red">(NEW)</span>:
+* **2024 Fall**: <span style="color: red">(NEW)</span>:
+    * EE698K - Programming for Signal Processing [[link]](stuff/2024_PSP.md)
+* **2024 Spring**: 
     * EE952 - Introduction to Machine Learning (for e-masters) [[link]](stuff/2024_ML_emasters.md)
-* **2024 Spring**: <span style="color: red">(NEW)</span>:
+* **2024 Spring**: 
     * EE798B - Data Structures and Algorithms for Electrical Engineers [[link]](stuff/2024_DSA.md)
-* **2024 Spring**: <span style="color: red">(NEW)</span>:
+* **2024 Spring**: 
     * EE698R - Advanced Topics in Machine Learning [[link]](stuff/2024_ML2.md)
 * **2023 Fall**:
     * EE798P - Audio Representation Learning [[link]](stuff/2023_ARL.md)