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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7813 Paths
7813 Paths · page 434 / 782
A comprehensive learning path covering the number theory and algorithmic foundations of modern public-key cryptography. Learners will master modular arithmetic, primality testing, and the core algorithms behind RSA, Diffie-Hellman, and elliptic curve cryptography, along with essential security considerations.
This learning path guides AI/ML students through the fundamental machine learning algorithms, starting from essential mathematical prerequisites and progressing through supervised and unsupervised learning methods. It covers linear regression, gradient descent, decision trees, k-means, and k-nearest neighbors, emphasizing the underlying concepts and mathematical foundations.
This advanced graduate-level path covers the algorithmic foundations of compilers, focusing on parsing algorithms (LL and LR), data-flow analysis, and register allocation. It builds from formal language theory and data structures through syntax analysis, semantic analysis, optimization, and code generation, emphasizing the underlying algorithms and their complexity.
A structured learning path covering core CPU scheduling algorithms (FCFS, SJF, Priority, Round-Robin, and Multilevel Queue), grounded in essential OS process and scheduling concepts. Designed for university students with intermediate difficulty.
A focused learning path for systems programmers covering the core cache replacement policies (FIFO, LRU, LFU, optimal) and the working set model. It builds on OS and data structure fundamentals to explain how caches behave, how to implement policies, and how to reason about their performance trade-offs.
This learning path provides a structured journey through the fundamental algorithms and data structures that enable modern database systems to efficiently store, retrieve, and manipulate data. Starting with foundational computer science concepts, it progresses through storage structures, indexing, query processing, and transaction management, culminating in an integrated understanding of how these components work together.
This path explores the design and analysis of randomized algorithms, covering core probabilistic techniques, Las Vegas and Monte Carlo classifications, and key applications including random selection, hashing, and primality testing. It builds from probability theory and algorithm analysis foundations through advanced topics.
This advanced learning path equips data science students with the theory and practice of streaming algorithms. It covers probabilistic data structures like Bloom filters and Count-Min Sketch, sampling techniques such as reservoir sampling, and the underlying probability concepts, enabling efficient processing of massive data streams.
This advanced graduate-level learning path covers core algorithms in computational geometry, from fundamental data structures and algorithmic techniques to specific problems: convex hulls, line segment intersection, Voronoi diagrams, and Delaunay triangulations. It emphasizes theoretical foundations, complexity analysis, and the interconnections between these topics.
A graduate-level learning path exploring advanced graph theory topics and their algorithmic applications, including network flows, matchings, planar graphs, graph coloring, and advanced algorithmic techniques. The path builds from foundational concepts to research-level topics, emphasizing rigorous theory and algorithmic design.