Course Modules: Randomized Algorithms-ALL SECTIONS (SP21)

Welcome to CS49/249, Randomized Algorithms

Welcome to CS49/249, Randomized Algorithms Welcome to CS49/249, Randomized Algorithms

Grading Policy Page

Grading Policy Grading Policy 1

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
Honor Principle Page

Honor Principle Honor Principle 2

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
Are you ready? Quiz

Are you ready? Are you ready? 10 3

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Lecture 1 : Introduction, Checking Equality, Monte-Carlo

Lecture 1 : Introduction, Checking Equality, Monte-Carlo Lecture 1 : Introduction, Checking Equality, Monte-Carlo

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Problem 1 and 2* Attachment

Problem 1 and 2* Problem 1 and 2* 4

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Lecture 2 : Linearity of Expectation, QuickSort, Las Vegas

Lecture 2 : Linearity of Expectation, QuickSort, Las Vegas Lecture 2 : Linearity of Expectation, QuickSort, Las Vegas

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Problem 3 and 4* Attachment

Problem 3 and 4* Problem 3 and 4* 4

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Lecture 3 : Karger's Minimum Cut Algorithm

Lecture 3 : Karger's Minimum Cut Algorithm Lecture 3 : Karger's Minimum Cut Algorithm

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Problem 5 Attachment

Problem 5 Problem 5 4

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Lecture 4 : Estimation Algorithms, Chernoff Bounds

Lecture 4 : Estimation Algorithms, Chernoff Bounds Lecture 4 : Estimation Algorithms, Chernoff Bounds

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Problem 6 Attachment

Problem 6 Problem 6 4

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Lecture 5 : Median of Means Trick, General Chernoff

Lecture 5 : Median of Means Trick, General Chernoff Lecture 5 : Median of Means Trick, General Chernoff

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Problem 7 and 10* Attachment

Problem 7 and 10* Problem 7 and 10* 4

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete

Lecture 6 : Randomized Median Algorithm

Lecture 6 : Randomized Median Algorithm Lecture 6 : Randomized Median Algorithm

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Problem 8 and 9 Attachment

Problem 8 and 9 Problem 8 and 9 4

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Lecture 7 : Estimating #DNF solutions via Importance Sampling

Lecture 7 : Estimating #DNF solutions via Importance Sampling Lecture 7 : Estimating #DNF solutions via Importance Sampling

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Problem 11 Attachment

Problem 11 Problem 11 4

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Lecture 8 : Finding Similar Pairs via Importance Sampling

Lecture 8 : Finding Similar Pairs via Importance Sampling Lecture 8 : Finding Similar Pairs via Importance Sampling

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Problem 12 Attachment

Problem 12 Problem 12 4

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Lecture 9 : Estimating the Average Degree in an Undirected Graph

Lecture 9 : Estimating the Average Degree in an Undirected Graph Lecture 9 : Estimating the Average Degree in an Undirected Graph

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Problem 13 and 14* Attachment

Problem 13 and 14* Problem 13 and 14* 4

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Lecture 10 : Balls and Bins, Birthday Paradox, Max Load

Lecture 10 : Balls and Bins, Birthday Paradox, Max Load Lecture 10 : Balls and Bins, Birthday Paradox, Max Load

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Problem 15 Attachment

Problem 15 Problem 15 3

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete

Lecture 11 : Balls and Bins, Coupon Collector, Poisson Random Variables

Lecture 11 : Balls and Bins, Coupon Collector, Poisson Random Variables Lecture 11 : Balls and Bins, Coupon Collector, Poisson Random Variables

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Problem 16 Problem 16 3

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete

Lecture 12 : Balls and Bins, Poisson Approximation + Applications

Lecture 12 : Balls and Bins, Poisson Approximation + Applications Lecture 12 : Balls and Bins, Poisson Approximation + Applications

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Problem 17 and 18 Attachment

Problem 17 and 18 Problem 17 and 18 3

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Lecture 13 : Hashing, Dictionary Maintenance, Universal Hash Families

Lecture 13 : Hashing, Dictionary Maintenance, Universal Hash Families Lecture 13 : Hashing, Dictionary Maintenance, Universal Hash Families

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Problem 19 Problem 19 3

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Lecture 14 : Fingerprinting

Lecture 14 : Fingerprinting Lecture 14 : Fingerprinting

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Problem 20 Attachment

Problem 20 Problem 20 3

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark done Must mark this module item done in order to complete Marked done Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete

Lecture 15 : Perfect Hashing

Lecture 15 : Perfect Hashing Lecture 15 : Perfect Hashing

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Problem 21 Attachment

Problem 21 Problem 21 3

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Lecture 16 : Streaming I : Count-Min

Lecture 16 : Streaming I : Count-Min Lecture 16 : Streaming I : Count-Min

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Problem 22 Attachment

Problem 22 Problem 22 2

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Lecture 17 : Streaming II : Count-Sketch, Estimating F2

Lecture 17 : Streaming II : Count-Sketch, Estimating F2 Lecture 17 : Streaming II : Count-Sketch, Estimating F2

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Lecture 18 : Streaming III : Reservoir Sampling, Estimating F_k

Lecture 18 : Streaming III : Reservoir Sampling, Estimating F_k Lecture 18 : Streaming III : Reservoir Sampling, Estimating F_k

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Lecture 19 : Streaming IV : Distinct Elements, Flajolet-Martin

Lecture 19 : Streaming IV : Distinct Elements, Flajolet-Martin Lecture 19 : Streaming IV : Distinct Elements, Flajolet-Martin

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Lecture 20 : Streaming V : More on Distinct Items, BJKST

Lecture 20 : Streaming V : More on Distinct Items, BJKST Lecture 20 : Streaming V : More on Distinct Items, BJKST

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Lecture 21 : Streaming VI : Distinct Elements with Deletion

Lecture 21 : Streaming VI : Distinct Elements with Deletion Lecture 21 : Streaming VI : Distinct Elements with Deletion

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Problem 27 Attachment

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Lecture 22 : The Experts Problem

Lecture 22 : The Experts Problem Lecture 22 : The Experts Problem

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Lecture 23 : Randomized Majority

Lecture 23 : Randomized Majority Lecture 23 : Randomized Majority

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Lecture 24 : Follow the Perturbed Leader

Lecture 24 : Follow the Perturbed Leader Lecture 24 : Follow the Perturbed Leader

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Lecture 25 : Randomization and Approximation Algorithms

Lecture 25 : Randomization and Approximation Algorithms Lecture 25 : Randomization and Approximation Algorithms

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Lecture 26 + 27 : k-means++

Lecture 26 + 27 : k-means++ Lecture 26 + 27 : k-means++

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lec26+27.pdf lec26+27.pdf 1

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Problem 30 Attachment

Problem 30 Problem 30 2

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Lecture 28 : Wrap-up, AMA

Lecture 28 : Wrap-up, AMA Lecture 28 : Wrap-up, AMA

Randomized Algorithms.pptx Attachment

Randomized Algorithms.pptx Randomized Algorithms.pptx 2

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The Alive Problem Sets

The Alive Problem Sets The Alive Problem Sets

The Problems Attachment

The Problems The Problems 1

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Hints (But peek at them only after an attempt) Attachment

Hints (But peek at them only after an attempt) Hints (But peek at them only after an attempt) 2

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Coding Ideas Attachment

Coding Ideas Coding Ideas 3

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