Tim Roughgarden's Online Courses
MOOCs on Coursera
based on Stanford's undergraduate algorithms course (CS161).
Comprises four 4-week courses:
- Part 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms
- Covers asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
- Part 2: Graph Search, Shortest Paths, and Data Structures
- Covers data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
- Part 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
- Covers greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
- Part 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
- Covers shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).
These courses have appeared on various "top MOOCs of all time" lists, like
here and here.
New sessions begin every few weeks.
This specialization subsumes the following older versions:
Stanford lectures on YouTube
Lectures on the Design
and Analysis of Algorithms, covering most of the above Coursera MOOCs,
for those of you who prefer blackboard lectures
(from Stanford's CS161, Winter 2011).
- Warning/apology: the audio is suboptimal on a few segements
of these lectures.
20 Video Lectures from A Second Course in Algorithms
(Stanford's CS261, Winter 2016).
Lecture notes are here
- Topics include: algorithms for maximum flow and minimum cuts
(augmenting paths, push-relabel etc.); bipartite matching and
generalizations; linear programming; duality; online
regret-minimization and the multiplicative weights algorithm; online
algorithms; design and analysis of approximation algorithms; the
traveling salesman problem; essential tools for the analysis of
randomized algorithms; beating brute-force search for NP-hard
20 Video Lectures on Algorithmic Game Theory
(from Stanford's CS364A, Fall 2013). Lecture notes are here
- Topics include: auction and mechanism design (Vickrey and Myerson
auctions, VCG mechanism, etc.); the "price of anarchy" (selfish
routing, smoothness, etc.); learning in games (no-regret algorithms,
etc.); complexity of Nash equilibria (PLS- and PPAD-completeness).
Case studies include: keyword search auctions, combinatorial auctions for spectrum, kidney exchange, and communication network management.
20 Video Lectures on Advanced Mechanism Design
(from Stanford's CS364B, Winter 2014). Lecture notes are here
- Topics include:
combinatorial auctions; Walrasian equilibria and the gross substitutes
ascending and ex post implementations; truthful approximation mechanisms;
black-box reductions; simple auctions and equilibrium guarantees for
them (Bayes-Nash smoothness, etc.);
Border's theorem; multi-parameter revenue-maximization.
20 Video Lectures on Beyond Worst-Case Analysis
(from Stanford's CS264, Fall 2014). Lecture notes are here
- Topics include: instance optimality;
parameterized analysis and condition numbers;
models of data
(pseudorandomness, locality, diffuse adversaries, etc.);
planted and semi-random graph models;
exact recovery (compressive sensing, LP decoding).
Motivating problems drawn from online algorithms, machine learning,
computational geometry, graph partitioning, linear programming,