Class Schedule

Event TypeDateTopicReferencesAnnouncements
Lecture Jan 22 Class overview. Reservoir Sampling Presentation-1
Mini-Ex 1 due on 1/31
Lecture Jan 24 Probability Review. Expectation, Variance, Markov Inequality, Chebyshev’s Inequality Presentation-2
Lecture Jan 29 Chernoff Bound Presentation-3
Lecture Jan 31 Sampling/Hashing Presentation-4
Presentation-5 (Hashing)
Lecture Feb 5 Bloom filter Presentation-6
Bloom Filter-1
Bloom Filter-2
Lecture Feb 7 Data Streaming Algorithms and Heavy Hitter Presentation-7 Homework added, Due Date: Feb 19
Lecture Feb 12 Count-Min Sketch Presentation-7
lecture note
Lecture Feb 14 Lower bounds for Streaming Algorithms slides
Lecture Feb 19 No Class, Monday Time-table Homework Due today
Midterm 1 Feb 21 In-class exam
Lecture Feb 26 Frequency Moment Estimation Presentation-9
lecture note
Lecture Feb 28 No class
Lecture Mar 5 Frequency Moment Estimation Presentation-9
Lecture Mar 7 Finding similar items Presentation-10
No class Mar 12 Spring recess
No class Mar 14 Spring Recess
Lecture Mar 19 Locality Sensitive Hashing Presentation-11
lecture note
Lecture Mar 21 Locality Sensitive Hashing Presentation-11
lecture note
Homework 2 Posted
Lecture Mar 26 Introduction to MapReduce Presentation-7
Lecture Mar 28 Graph algorithms on MapReduce Presentation-7
Lecture Apr 2 Graph algorithms on MapReduce mapreduce-notes
Lecture Apr 4 Demo on how to write MapReduce code mapreduce-demo
rowcolumntest.txt testData.txt
Homework 2 Due
Lecture Apr 9 Clustering: k-means, k-means++, k-center, k-median Presentation-15
lecture note
MiniExercise 2 Posted
Lecture Apr 11 Correlation Clustering Presentation-16
Lecture Apr 16 Exam Overview
Midterm 2 April 18 In-class exam
Lecture Apr 23 Interactive Clustering Presentation-17
lecture notes
Lecture Apr 25 Learning Algorithms Presentation-18
Ch 12 : section 12.1,12.2 from
textbook by Leskovec
Perceptron ref
MiniExercise 2 due on April 28
Lecture Apr 30 Learning Algorithms Presentation-19
Ch 12 : section 12.3 from
textbook by Leskovec