Academic Resource Forum

Truth, Justice, and Algorithms by Professor Ariel Procaccia Date Topic Teacher Slides and Links 1/11 Social choice : basic concepts Procaccia slides 1/13 Social choice : complexity of manipulation Procaccia slides , paper 1/18 Martin Luther King Day 1/20 Social choice : advanced manipulation Procaccia slides 1/25 Social choice : ranking and selection systems Procaccia slides , paper 1/27 Social choice : voting rules as maximum likelihood estimators Procaccia slides , paper 1 , paper 2 2/1 Fair division : cake cutting algorithms Procaccia slides 2/3 Fair division : complexity of proportionality Procaccia slides , paper 2/8 Fair division : rent division and computational resources Procaccia s...

Tim Roughgarden's Lecture Notes (Click on one of the following courses to expand.) Modern Algorithmic Toolbox (with Greg Valiant ) (CS168, spring 2017) Lecture 1: Introduction and Consistent Hashing Lecture 2: Approximate Heavy Hitters and the Count-Min Sketch Lecture 3: Similarity Metrics and kd-Trees Lecture 4: Dimensionality Reduction Lecture 5: Generalization (How Much Data Is Enough?) Lecture 6: Regularization Lecture 7: Understanding and Using Principal Component Analysis (PCA) Lecture 8: How PCA Works Lecture 9: The Singular Value Decomposition (SVD) and Low-Rank Matrix Approximations Lecture 10: Tensors, and Low-Rank Tensor Recovery Lectures 11 and 12: Spectral Graph Theory Lecture 13: Sampling and Estimation Lecture 14: Markov Chain Monte Carlo Lectures 15 and 16: The Fourier Transform and Convolution Lecture 17: Compressive Sensing Lecture 18: Linear and Convex Programming, with Applications to Sparse Recove...

Official blurb : In Algorithms Illuminated , Tim Roughgarden teaches the basics of algorithms in the most accessible way imaginable. This Omnibus Edition contains the complete text of Parts 1-4, with thorough coverage of asymptotic analysis, graph search and shortest paths, data structures, divide-and-conquer algorithms, greedy algorithms, dynamic programming, and NP-hard problems. Hundreds of worked examples, quizzes, and exercises, plus comprehensive online videos, help readers become better programmers; sharpen their analytical skills; learn to think algorithmically; acquire literacy with computer science's greatest hits; and ace their technical interviews. Videos and additional resources: (Click on one of the following topics to expand.) Videos (Part 1) Full playlist Why Study Algorithms? (Section 1.1) Integer Multiplication (Section 1.2) Karatsuba Multiplication (Section 1.3) MergeSort: Motivation and Example (Sect...