Learn from IIIT, on the fly.
Concise revision notes, ultra-condensed cheatsheets, high-yield topics, and practice questions for IIIT-H courses — designed for fast pre-exam prep.
An unofficial study project. No lecture slides or past papers are hosted here. All content is paraphrased and original.
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Computer Vision
Computer Vision (Spring 2025-26). Prerequisite units recap the foundations: introduction (Marr, Three Rs, Gestalt), digital image processing (filters, histograms, Fourier/DCT, morphology, Hough), machine learning (logistic regression, NNs, ensembles, RNN, metrics, PCA/SVD, clustering), and convolutional neural networks (LeNet → AlexNet → VGG → Inception → ResNet → DenseNet → SENet → MobileNet → EfficientNet). The main course then covers object detection, dense prediction, pose estimation, 3D representations, NeRF & 3D Gaussian Splatting, Transformers and Vision Transformers, self-supervised learning (SimCLR, DINO, MAE, JEPA), modern transformer engineering, vision-language models, and video understanding. This revision hub distils the entire syllabus into chapter-wise notes, cheatsheets, high-yield topics and practice questions — designed to revise the whole course in an evening, not a week.
Behavioral Research: Statistical Methods
The statistical methods that turn behavioural data into reliable inferences. Builds from probabilistic intuition (Bayes, base rate) through hypothesis testing, ANOVA, regression, Bayesian methods, and logistic regression. Heavy emphasis on which test to pick, reporting effect sizes, and avoiding p-hacking.
Distributed Systems
Distributed Systems (Monsoon 2025-26) covers the foundations of building correct, fault-tolerant, scalable systems out of many independent computers. The course spans CAP and impossibility results; logical and physical clocks; global snapshots; causal-order message delivery; distributed mutual exclusion; deadlock detection; consensus and Byzantine agreement; transaction commit protocols (2PC, 3PC); Raft for replicated state machines; the GHS distributed minimum spanning tree algorithm; and the Google File System as a flagship distributed storage case study. This revision hub distils the entire syllabus into chapter-wise notes, cheatsheets, high-yield topics and practice questions — designed to revise the whole course in an evening, not a week.
Technology Product Entrepreneurship
How to take a deeptech idea from a vague spark to an investor-ready startup. Built around four phases (Idea → Hypothesis | Problem-Solution Fit | Product-Market Fit | Go-To-Market) and a coherent toolkit (Idea Hexagon, Five Filters, 5Ws, BML loop, Validation Board, Value Proposition Canvas, Business Model Canvas, Lean Canvas, STP, AHA Grid, USP Defensibility, SWOT, Find-Your-USP Venn). Capstone is an investor pitch designed to ignite FOMO and calm FOLS.
More courses on the roadmap — search above to check if yours is planned.