Lecture 21 of 40Week 5
Lecture 21 : Randomized Numerical Linear Algebra:a) Matrix multiplication + QB decomposition
Prof. Anirban Dasgupta and Prof. Sourangshu Bhattacharya · IIT Gadhinagar IIT Kharagpur
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All the lectures
- 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 (Contd.)
- 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 (Contd.)
- Lecture 18 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Contd.)
- Lecture 19 : Randomized Numerical Linear Algebra: Random projection
- Lecture 20 : Randomized Numerical Linear Algebra: Random projection (Contd.)
- 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(Contd.)
- Lecture 29 : Hadoop System(Contd.)
- Lecture 30 : Spark
- Lecture 31 : Spark(Contd.)
- Lecture 32 : Spark(Contd.)
- Lecture 33 : Distributed Machine Learning and Optimization: Introduction
- Lecture 34 : SGD+Proof
- Lecture 35 : SGD+Proof(Contd.)
- Lecture 36 : Distributed Machine Learning and Optimization: ADMM + applications
- Lecture 37 : Distributed Machine Learning and Optimization: ADMM + applications(Contd.)
- Lecture 38 : Clustering
- Lecture 39 : Clustering(Contd.)
- Lecture 40 : Conclusion