§ 40 chapters · 74 guides total

All Chapters

Every chapter in the wiki. Each chapter holds a handful of guides — search, sort, or jump straight to a chapter code.

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40 chapters

Code	Chapter	Dept	Guides
CS 101	Intro to Programming Variables, types, control flow, functions, modules, errors — the Python you need before any other course in this curriculum.	CS	4
CS 102	Computational Tools The terminal, git, debuggers, environments — the tooling every CS student needs but most curricula skip teaching.	CS	1
CS 201	Data Structures Stacks, queues, trees, heaps, tries, hashmaps — the containers every algorithm leans on, with implementations in Python and the problems that exercise them.	CS	6
CS 202	Python Idioms Decorators, context managers, dataclasses, generators, typing, logging — the idiomatic Python a working engineer writes daily.	CS	6
CS 301	Algorithms Binary search, sorting, recursion, DP, greedy, backtracking, graph algorithms — the named algorithms that solve named problems.	CS	14
CS 302	Coding Patterns Two pointers, sliding window, prefix sum, monotonic stack/deque, intervals — the recurring problem shapes that show up across the interview catalog.	CS	7
CS 303	SQL I — Reading Data SELECT, JOIN, GROUP BY, subqueries, CTEs, set operations — reading data out of relational stores. The 80% of SQL you'll use daily.	CS	10
CS 304	Operating Systems Processes, threads, scheduling, memory management, the GIL — what's actually running underneath when your Python code runs.	CS	1
CS 305	Computer Architecture Number representation, the memory hierarchy, cache, pipelining, locality — the hardware mental model that explains why some code is fast and some is slow.	CS	0
CS 401	Computer Networks TCP/IP, HTTP, DNS, TLS, WebSockets, latency vs throughput — the network stack every backend and ML service touches.	CS	0
CS 402	SQL II — Analytical Window functions, ranking, recursive CTEs, time-series queries, JSON, star schemas — SQL beyond SELECT and JOIN.	CS	7
CS 403	NumPy & Pandas Arrays, broadcasting, vectorization, dataframes, groupby, joins — numeric and tabular work in Python. The substrate every ML training script sits on.	CS	6
CS 404	Web Backend Basics Routes, requests, responses, middleware, auth, sessions — building HTTP APIs in Python. The minimum you need before you can serve a model.	CS	0
CS 405	ML Math Gradients of vectors and matrices, the chain rule that powers backprop, optimization basics, common loss landscapes — the calculus that makes deep learning work.	MATH	0
CS 502	Software Engineering Practices Testing, profiling, packaging, logging, observability, dependency management — what turns code that runs into code that ships and stays alive.	CS	5
CS 503	SQL III — Systems Indexes, query plans, transactions, isolation levels, normalization, partitioning, OLTP vs OLAP — running a database in production, not just querying one.	CS	7
CS 504	Backend for ML FastAPI, Docker, queues, Ray/Modal, GPU scheduling, observability for endpoints — putting a model behind an HTTP endpoint that survives traffic.	CS	0
CS 505	Concurrency & Parallelism Threads, processes, asyncio, locks, deadlocks, message passing, work queues — multiple things happening at once, correctly.	CS	0
CS 605	Distributed Systems Replication, consensus, sharding, eventual consistency, RPCs, distributed training basics — what happens when one machine isn't enough.	CS	0
MATH 101	Discrete Math Sets, logic, proof techniques, induction, counting, asymptotic notation — the discrete math vocabulary CS leans on everywhere.	MATH	0
MATH 102	Calculus I Limits, continuity, derivatives, integrals, the fundamental theorem — single-variable calculus, the gateway to everything multivariate.	MATH	0
MATH 103	Linear Algebra I Vectors, dot products, matrices, systems of equations, basic transformations — the algebra of data.	MATH	0
MATH 201	Calculus II Partial derivatives, gradients, multiple integrals, Lagrange multipliers — calculus for optimization and the multivariate world ML lives in.	MATH	0
MATH 202	Linear Algebra II Eigenvalues, SVD, vector spaces, orthogonality, projections — the linear algebra ML actually uses, not just the freshman version.	MATH	0
MATH 203	Probability & Statistics Random variables, common distributions, expectation, the CLT, hypothesis testing, Bayesian intuition — the probability you need before any ML course.	MATH	0
ML 501	Classical Machine Learning Linear models, trees, ensembles, regularization, cross-validation, evaluation metrics — the right tool for most tabular problems, and the foundation deep learning generalises.	ML	0
ML 601	Deep Learning Fundamentals Backprop, activations, optimizers, regularization, training dynamics, common architectures — the mechanics underneath every modern model.	ML	0
ML 602	Computer Vision CNNs, ViTs, detection, segmentation, augmentation, transfer learning — the vision stack from pixels to predictions.	ML	0
ML 603	NLP Foundations Tokenization, embeddings, sequence models, attention, the pre-transformer NLP toolkit and how it generalises.	ML	0
ML 604	GPU Programming CUDA basics, kernels, GPU memory hierarchy, profiling, the mental model behind why PyTorch ops look the way they do.	ML	0
ML 701	LLMs & Transformers Transformer architecture, attention variants, pretraining, fine-tuning, RLHF, scaling laws — how large language models actually work end-to-end.	ML	0
ML 702	Reinforcement Learning Markov decision processes, value iteration, Q-learning, policy gradients, PPO — learning from interaction instead of labels.	ML	0
ML 703	MLOps Experiment tracking, model registry, CI/CD for models, monitoring drift, A/B testing, rollback — keeping ML systems alive in production.	ML	0
ML 704	Vector Databases & RAG Embeddings, approximate nearest-neighbour search, hybrid retrieval, chunking strategies, evaluation — retrieval-augmented generation in practice.	ML	0
ML 705	Prompting & Eval Few-shot, chain-of-thought, structured outputs, function calling, eval design — prompting techniques that survive harder eval, not anecdote.	ML	0
ML 801	Agents & Tool Use Tool use protocols, planning, ReAct, multi-agent coordination, safety wrappers — wiring LLMs into systems that act in the world.	ML	0
ML 802	AI Safety & Alignment Prompt injection, jailbreaks, red teaming, refusal training, RLHF alignment, eval frameworks — making AI systems that fail safely.	ML	0
ML 803	Production Systems Design Capacity planning, latency budgets, caching, A/B testing, cost modeling, scaling strategies — designing ML systems that survive growth.	ML	0
ML 804	Research Practice Reading papers, replicating results, experiment design, writing well, the academic publishing process — the meta-skill underneath everything else.	ML	0
ML 805	Capstone An end-to-end project: pick a real problem, ship a working system, write it up. The capstone that proves the curriculum.	ML	0