CS439 - Algorithmic Foundations of Big Tech

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Course Description:
This course explores into the fundamental algorithms and mathematical principles that drive the operations of major IT companies. Students will gain an in-depth understanding of foundational topics such as Information Retrieval, Web Search, Recommendation Systems, Online Advertisement and Ad Placement, Crowdsourcing, Ratings Aggregation, Voting, Social Network Algorithms, Influence Maximization, Public Key Cryptography, Information Propagation and Content Viralization. Through a series of modules, the course will examine how companies like Google, Amazon, Netflix, Meta, Twitter, and Visa/Mastercard utilize these algorithms to optimize search engines, personalize recommendations, aggregate ratings and reviews, and ensure secure ePayment transactions. With a focus on real-world applications, students will gain hands-on experience in solving complex problems central to the success of today's big IT industries.

Prerequisites:

• An undergraduate course in Algorithms, Data Structures, and Linear Algebra.
• Strong problem solving skills, background in data science and machine learning

Lectures:

Topic	Slides
Information Retrieval and Web Search Text Preprocessing and Representation: Vector Space Modeling Inverted Index, Boolean and Ranked Retrieval Web Search Content Spamming, Trustworthy Webpages and Node Centrality PageRank: Dangling Nodes, Spider Traps, Random Teleporting Link Spamming Personalized and Topic Sensitive Pagerank Hyperlink-Induced Topic Search	Slides
Recommendation Systems Recommenders: Motivation and Applications Problem Formulation and Evaluation Methods and Techniques (anova and Averages) Content Based Filtering Collaborative Filtering Matrix Factorization Rank Factorization Low-Rank Structure and Singular Value Decomposition Hybrid Methods for Recommendation Systems	Slides
Rating Aggregation Crowdsourcing: Applications and Techniques Crowdsourcing: Findmax Mathematical foundations of Rating Aggregation Bayesian Ranking: Methodology and Real-world applications Ensemble Learning Voting Theory Ranking Aggregation	Slides
Web Advertisement Web Advertisement: Types and Issues Ad Effectiveness and Cost Metrics Ad placement and selection Offline vs Online Algorithms Matching Ads to Slots Mechanism Design vs Algorithm Design Introduction to Auctions and types of Auctions GSP and VCG Auctions	Slides
Number Theory & Cryptography Divisibility and Congruence Modular Arithmetic and its Applications GCD, (Extended) Euclidean Algorithm, Relative Prime The Caesar Cipher and Affine Cipher, Modular Inverse The Chinese Remainder Theorem Fermat’s Little Theorem and Modular Exponentiation Private and Public Key Cryptography, The RSA Cryptosystem	Slides
ePayment Security E-Payments: Introduction, Security and Confidentiality Mastercard/Visa: Techniques for Secure and Confidential Transactions Overview of E-Payment Protocols: SSL, SET, and 3-D Secure Online Payment Risks: Challenges and Emerging Threats Emerging Technologies in E-Payments: NFC, Crypto and Blockchain RSA: The Foundation of Modern Cryptosystems The Future of E-Payments	Slides
Content Viralization Web 2.0 and User Generated Content Understanding Viral Content Paths to Viralization Youtube, Instagram and Tiktok’s Recommendation System Network Effect Sequential Decision Model Information Cascade	Slides
Social Networks Analysis Social Networks Analysis and its Applications Network Descriptive and Network Connectivity Analytics Heavy-Tailed Degree Distributions Small World Phenomenon Large-Scale Network Structure Community Structures in Networks Community Detection Algorithms Community Detection in Temporal/Dynamic Networks	Slides
Social Influence Topology-Dependent Static Influence Models Link Importance: Strong and Weak Ties Influence Dynamics in Social Networks and Applications: Epidemic-Based Models Activation-Based Models (Discrete Time) Models for Opinion Dynamics Continuous-Time Diffusion Models Influence Maximization Influence: Theory versus Practice	Slides

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