Open Electives

ATP

Late Dropped by Contributor

• Psychological Refractory Period
• Stroop Task
• Action Potential
• Intracellular & Extracellular Recording
• fMRI
• Electroencephalography (EEG)

CG

• Peter Shirley, Fundamentals of Computer Graphics (course book, often reffered)
• SIGGRAPH Intro to opengl video
• docs.gl

CN

• Kurose
• Gate Smashers
• Computerphile for some topics
• Kunal Kushwaha
• Knowledge Gate

COO

• Stanford Lectures
• Totally Unimodular Matrices
• Totally Unimodular Matrices: More Indepth
• Convex Cones and Farkas' Lemma
• Fair and Envy-Free Cake Cutting
• Assignment Problem: Integer Linear Programming
• Playlist with some stuff on NLPP
• Lecture 15 and 16 for Primal to Dual Conversion
• Page 2 for Epigraph related stuff
• Game Theory Related Stuff
• Sensitivity Analysis
• Game Theory Lectures: The playlist has very good proofs for Sperner's Lemma and Brouwer's Fixed Point Theorem
• Anti-recommendation --> lecture notes

CV

• Stanford Computer Vision Lecture Notes
• CMU 16-385 Slides

DL

• Stanford CS224N: NLP with Deep Learning
• Andrew NG - DeepLearningAI

DSCD

• Distributed Systems - Tanenbaum
• RPC/gRPC
• Network Time Protocol (NTP)
• RabbitMQ
• Distributed Systems by Martin Kleppmann (Good Watch)
• Sequential and Causal Consistency
• Dominant Resource Fairness: Fair Allocation of Multiple Resource Types

DSc

• Stats Playlist
• Hypothesis Testing
• Some lectures from MIT 6.854 (Advanced Algorithms)
• JL Lemma
• SVD
• SVD v/s Eigen Values
• Statquest

EVS

• All answers compiled

FCS

• PicoCTF
• CryptoHack
• Prof Ninja
• Ofcourse, none of the above is "required" for the course and your proficiency is inversely proportional to the grade you might obtain

FF

• 14th and 15th edition of Fundamentals of Financial Management (Eugene F. Brigham, Joel F. Houston) along with some question papers of 2023
• Do attend lectures as they are the most important source of learning in this course.
• The make-up quiz is very hard, so try not to rely on it and work hard before each quiz.

GMT

• An introduction to game theory, Martin J Osborne (available online)

GPU

• Nvidia CUDA programming
• Programming Massively Parallel Processors - A Hands-on Approach
• Other course content like OpenMP/OpenCL | Read the docs

GT

• Introduction to Graph Theory by Douglas B. West
• Note, the course is not on algorithms

IR

• Old IIIT Recordings
• Indexing and VSM
• Some videos from Mining Massive Datasets Course (Stanford)
• Naveen Aggarwal's Playlist (Panjab University)
• Information Retrieval and Web Search IISER Kolkata

ITS

• Sharam nahi aayi?
• Aayi?

InT

• Handwritten notes of 'Prof. Manuj Mukherjee'
• Reference book is mostly not needed. Attend classes for this course, this might be the best course you have seen in the college

KCES

• Sharam nahi aayi?
• Aayi?

ML

• Mahesh Huddar's Videos
• Statquest

NLP

• NPTEL Course
• Smoothing
• Left Recursion/ Left Factoring
• Earley Parser
• Ritvik Math Playlist
• Stanford CS224N: NLP with Deep Learning
• Stanford CS224U: Natural Language Understanding

NSC

Do prev. years

• Neso Academy Playlist
• RSA (With Extended Euclidean Algorithm)
• Linear Feedback Shift Register
• Public Key Cryptography
• Key Distribution Center (KDC)

NSS-1

If you want an easier time with the course, take it in the 7th semester after doing CN and maybe FCS(do note these are two very different courses)

• For Stream and Block Ciphers - Dan Boneh's YouTube Channel
• A very good explanation of Kerberos
• For other cryptographic concepts Computerphile
• For SSL/TLS, this Cloudflare article, and this article for TLS1.2 and TLS1.3
• For IPSec, this strongswan article
• Buffer Overflow and Format String Vulnerability - Team bi0s wiki and This binary exploitation notes
• Lectures the biggest resource, right after asking the prof for doubts

NSS-2

This course is very hands on. Expect to be reading lots of man pages, documentation, and setting up things. Some concepts of NSS-1 are talked about in brief. Use the previous resources to brush up your knowledge.

• Tor white paper
• Lots of papers exist on attacks against Tor. Prof will post resources on gc.
• Active Directory Lots of extra material here as well. Only do what's necessary
• Just attend lectures and read notes. Should be sufficient.

PB

• www.google.com

PRMP

• Slides from 'HIPEC/Prof. Vivek Kumar cse513 offering'
• gdb tutorial You would need it (seriously)
• Most of the course is discussing research papers. Additional resources not needed

QM

• JJ Sakurai (Can download online)
• Physics Libretexts (Essential Graduate Physics)
• extracts of MIT OCW Lectures

RL

• BartoSutton (41MB)
• Bertsekas (extra-ref)
• Keypapers in RL
• UPenn's DL Course Week 11
• RL Course NTNU
• Select Lectures on MDP Stanford CS221
• CS234 Stanford
• DeepMind x UCL | Introduction to Reinforcement Learning 2015
• DeepMind x UCL | Reinforcement Learning Course 2018
• DeepMind x UCL | RL Lecture Series 2021 (Playlist Name has Deep Learning but probably a typo as video names have RL)
• Reinforcement Learning by the Book
• RL Relevant Playlists from CS 486/686 UoT (L18-L21)
• NPTEL Course - Heard good things online, did not use personally

VPM

• Essentials of Investments, 12th Edition (Zvi Bodie Professor, Alex Kane etc.) [solutions available]

WN

• Links I found useful (Not exhaustive)

iROB

• (Peter Corke, Second Edition) Robotics, Vision and Control
• MATLAB Simulink resources