Probability and CountingSTAT 110 · Probability · Lec 1·Combinatorics & Graph Theory · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—46:29
The unreasonable effectiveness of the convexity assumption in high dimensionsMathematics·Research Seminar·Convex Optimization · Bo'az Klartag · UC BerkeleyBo'az KlartagUC Berkeley—52:23
Pigeonhole PrinciplePrinciples of Discrete Applied Mathematics · Lec 1·Category Theory, Topos Theory & Set Foundations · Ankur Moitra · MITAnkur MoitraMIT—1:13:47
The Mathematics of Crime: Predictive Policing and Gang ActivityMathematics·Public Colloquium·Combinatorics & Graph Theory · Andrea L. Bertozzi · UCLAAndrea L. BertozziUCLA—1:12:49
Independence and ConditioningPrinciples of Discrete Applied Mathematics · Lec 2·Statistical Learning & Causality · Ankur Moitra · MITAnkur MoitraMIT—1:10:58
Inclusion-ExclusionPrinciples of Discrete Applied Mathematics · Lec 3·Probability & Limit Theorems · Ankur Moitra · MITAnkur MoitraMIT—1:19:29
Jensen's Inequality: Intuition and ApplicationsOptimization Bootcamp: Convex Optimization for ML & Control · Lec 8·Probability & Limit Theorems · Steve Brunton · University of WashingtonSteve BruntonUniversity of Washington5 ch14:22
Story Proofs, Axioms of ProbabilitySTAT 110 · Probability · Lec 2·Combinatorics & Graph Theory · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—45:40
The Central Limit Theorem: Sums of Random Variables & ConvolutionsMathematics·Standalone Lecture·Probability & Limit Theorems · Grant Sanderson · 3Blue1BrownGrant Sanderson3Blue1Brown11 ch31:14
Tail BoundsPrinciples of Discrete Applied Mathematics · Lec 8·Probability & Limit Theorems · Ankur Moitra · MITAnkur MoitraMIT—1:20:53
Chernoff BoundsPrinciples of Discrete Applied Mathematics · Lec 9·Probability & Limit Theorems · Ankur Moitra · MITAnkur MoitraMIT—55:18
Birthday Problem, Properties of ProbabilitySTAT 110 · Probability · Lec 3·Combinatorics & Graph Theory · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—48:54
Gambler's Ruin and Random VariablesSTAT 110 · Probability · Lec 7·Stochastic Processes & Markov Chains · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—51:45
Conditional ProbabilitySTAT 110 · Probability · Lec 4·Statistical Learning & Causality · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—49:45
Conditioning Continued, Law of Total ProbabilitySTAT 110 · Probability · Lec 5·Probability & Limit Theorems · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—50:01
Definitions and InequalitiesMatrix Methods in Data Analysis, Signal Processing, and Machine Learning · Lec 20·Probability & Information Theory · Gilbert Strang · MITGilbert StrangMIT—55:01
Monty Hall, Simpson's ParadoxSTAT 110 · Probability · Lec 6·Probability & Limit Theorems · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—49:00
Saddle Points Continued, Maxmin PrincipleMatrix Methods in Data Analysis, Signal Processing, and Machine Learning · Lec 19·Probability & Information Theory · Gilbert Strang · MITGilbert StrangMIT—52:13
Random Variables and Their DistributionsSTAT 110 · Probability · Lec 8·Probability & Limit Theorems · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—50:24
Expectation, Indicator Random Variables, LinearitySTAT 110 · Probability · Lec 9·Probability & Limit Theorems · Joe Blitzstein · Harvard UniversityJoe BlitzsteinHarvard University—50:23