Lecture 1 - Background: Introduction
Lecture 2 - Probability: Concentration inequalities
Lecture 3 - Linear algebra: PCA, SVD
Lecture 4 - Optimization: Basics, Convex, GD
Lecture 5 - Machine Learning: Supervised, generalization, feature learning, clustering.
Lecture 6 - Memory-efficient data structures: Hash functions, universal / perfect hash families
Lecture 7 - Bloom filters
Lecture 8 - Sketches for distinct count
Lecture 9 - Sketches for distinct count (Continued...)
Lecture 10 - Misra-Gries sketch
Lecture 11 - Frequent Element: Space Saving and Count Min
Lecture 12 - Frequent Element: Count Sketch
Lecture 13 - Near Neighbors
Lecture 14 - Locality Sensitive Hashing
Lecture 15 - Building LSH Tables
Lecture 16 - Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants
Lecture 17 - Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Continued...)
Lecture 18 - Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Continued...)
Lecture 19 - Randomized Numerical Linear Algebra: Random projection
Lecture 20 - Randomized Numerical Linear Algebra: Random projection (Continued...)
Lecture 21 - Randomized Numerical Linear Algebra: a) Matrix multiplication + QB decomposition
Lecture 22 - Randomized Numerical Linear Algebra: b) CUR+CX
Lecture 23 - Randomized Numerical Linear Algebra: a) L2 regression using RP
Lecture 24 - Randomized Numerical Linear Algebra: b) Leverage scores
Lecture 25 - Randomized Numerical Linear Algebra: c) Hash Kernels + Kitchen Sink
Lecture 26 - Map-reduce and Hadoop
Lecture 27 - Hadoop System
Lecture 28 - Hadoop System (Continued...)
Lecture 29 - Hadoop System (Continued...)
Lecture 30 - Spark
Lecture 31 - Spark (Continued...)
Lecture 32 - Spark (Continued...)
Lecture 33 - Distributed Machine Learning and Optimization: Introduction
Lecture 34 - SGD+Proof
Lecture 35 - SGD+Proof (Continued...)
Lecture 36 - Distributed Machine Learning and Optimization:ADMM + applications
Lecture 37 - Distributed Machine Learning and Optimization:ADMM + applications (Continued...)
Lecture 38 - Clustering
Lecture 39 - Clustering (Continued...)
Lecture 40 - Conclusion
