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Algorithms DS

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Foundations of Randomized Methods & Concentration Inequalities

Incomplete

02W02

Randomized SVD – I: Basics & Sampling Techniques

Incomplete

03W03

Randomized SVD – II: Applications to PCA & Dimensionality Reduction

Incomplete

04W04

Graph-Based Learning – I: Spectral Graph Theory, Clustering, Community Detection

Incomplete

05W05

Graph-Based Learning – II: Graph-Based Ranking

Incomplete

06W06

Dimension Reduction with Johnson-Lindenstrauss Lemma

Incomplete

07W07

Approximate Nearest Neighbors (ANN) – I: LSH & Similarity Search

Incomplete

08W08

Approximate Nearest Neighbors (ANN) – II: MinHash, SimHash, Bloom Filters

Incomplete

09W09

Randomized Methods for Regression

Incomplete

010W10

Matrix Sketching for Machine Learning

Incomplete

011W11

Streaming Algorithms – I: Count-Min Sketch, Heavy Hitters, Frequency Moments

Incomplete

012W12

Streaming Algorithms – II: Reservoir Sampling, Graph Streams, Streaming PCA

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

Count Sketch

Open this first for the core logic and traps.

Dive in

1 min

Frequency Estimation in Streams

Open this first for the core logic and traps.

Dive in

1 min

HyperLogLog

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // @IITMadrasBSDegreeProgramme

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSDA5003

PG / MTech

4 Credits

Algorithms for Data Science (ADS)

The aim of this second-level graduate course is to provide a broad overview and develop the tools and methods necessary for the large-scale problem...

Required Dependencies:

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Foundations of Randomized Methods & Concentration Inequalities

Module 2

Randomized SVD – I: Basics & Sampling Techniques

Module 3

Randomized SVD – II: Applications to PCA & Dimensionality Reduction

Module 4

Graph-Based Learning – I: Spectral Graph Theory, Clustering, Community Detection

Module 5

Graph-Based Learning – II: Graph-Based Ranking

Module 6

Dimension Reduction with Johnson-Lindenstrauss Lemma

Module 7

Approximate Nearest Neighbors (ANN) – I: LSH & Similarity Search

Module 8

Approximate Nearest Neighbors (ANN) – II: MinHash, SimHash, Bloom Filters

Module 9

Randomized Methods for Regression

Module 10

Matrix Sketching for Machine Learning

Module 11

Streaming Algorithms – I: Count-Min Sketch, Heavy Hitters, Frequency Moments

Module 12

Syllabus

Topic

Foundations of Randomized Methods & Concentration Inequalities

Randomized SVD – I: Basics & Sampling Techniques

Randomized SVD – II: Applications to PCA & Dimensionality Reduction

Graph-Based Learning – I: Spectral Graph Theory, Clustering, Community Detection

Graph-Based Learning – II: Graph-Based Ranking

Dimension Reduction with Johnson-Lindenstrauss Lemma

Approximate Nearest Neighbors (ANN) – I: LSH & Similarity Search

Approximate Nearest Neighbors (ANN) – II: MinHash, SimHash, Bloom Filters

Randomized Methods for Regression

Matrix Sketching for Machine Learning

Streaming Algorithms – I: Count-Min Sketch, Heavy Hitters, Frequency Moments

Streaming Algorithms – II: Reservoir Sampling, Graph Streams, Streaming PCA

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Algorithms for Data Science (ADS)

Execution Protocol

Topic

Foundations of Randomized Methods & Concentration Inequalities

Randomized SVD – I: Basics & Sampling Techniques

Randomized SVD – II: Applications to PCA & Dimensionality Reduction

Graph-Based Learning – I: Spectral Graph Theory, Clustering, Community Detection

Graph-Based Learning – II: Graph-Based Ranking

Dimension Reduction with Johnson-Lindenstrauss Lemma

Approximate Nearest Neighbors (ANN) – I: LSH & Similarity Search

Approximate Nearest Neighbors (ANN) – II: MinHash, SimHash, Bloom Filters

Randomized Methods for Regression

Matrix Sketching for Machine Learning

Streaming Algorithms – I: Count-Min Sketch, Heavy Hitters, Frequency Moments

Streaming Algorithms – II: Reservoir Sampling, Graph Streams, Streaming PCA

Intelligence Hub