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101 - 110 of 217 results for: CS

CS 254: Computational Complexity

An introduction to computational complexity theory. Topics include the P versus NP problem; diagonalization; space complexity: PSPACE, Savitch's theorem, and NL=coNL; counting problems and #P-completeness; circuit complexity; pseudorandomness and derandomization; complexity of approximation; quantum computing; complexity barriers. Prerequisites: 154 or equivalent; mathematical maturity.
Terms: Spr | Units: 3
Instructors: Williams, R. (PI)

CS 255: Introduction to Cryptography

For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (symmetric and public key), digital signatures, data integrity, authentication, key management, PKI, zero-knowledge protocols, and real-world applications. Prerequisite: basic probability theory.
Terms: Win | Units: 3
Instructors: Boneh, D. (PI)

CS 259D: Data Mining for Cyber Security

The massive increase in the rate of novel cyber attacks has made data-mining-based techniques a critical component in detecting security threats. The course covers various applications of data mining in computer and network security. Topics include: Overview of the state of information security; malware detection; network and host intrusion detection; web, email, and social network security; authentication and authorization anomaly detection; alert correlation; and potential issues such as privacy issues and adversarial machine learning. Prerequisites: Data mining / machine learning at the level of CS 246 or CS 229; familiarity with computer systems and networks at least at the level of CS 110; CS 140 and CS 144 strongly recommended; CS 155 recommended but not required.
Terms: Aut | Units: 3-4
Instructors: Bahmani, B. (PI)

CS 261: Optimization and Algorithmic Paradigms

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms. Prerequisite: 161 or equivalent.
Terms: Spr | Units: 3

CS 262: Computational Genomics (BIOMEDIN 262)

Applications of computer science to genomics, and concepts in genomics from a computer science point of view. Topics: dynamic programming, sequence alignments, hidden Markov models, Gibbs sampling, and probabilistic context-free grammars. Applications of these tools to sequence analysis: comparative genomics, DNA sequencing and assembly, genomic annotation of repeats, genes, and regulatory sequences, microarrays and gene expression, phylogeny and molecular evolution, and RNA structure. Prerequisites: 161 or familiarity with basic algorithmic concepts. Recommended: basic knowledge of genetics.
Terms: Win | Units: 3

CS 263: Algorithms for Modern Data Models (MS&E 317)

We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of CS 261.
Terms: Spr | Units: 3
Instructors: Goel, A. (PI)

CS 264: Beyond Worst-Case Analysis

This course is motivated by problems for which the traditional worst-case analysis of algorithms fails to differentiate meaningfully between different solutions, or recommends an intuitively "wrong" solution over the "right" one. This course studies systematically alternatives to traditional worst-case analysis that nevertheless enable rigorous and robust guarantees on the performance of an algorithm. Topics include: instance optimality; smoothed analysis; parameterized analysis and condition numbers; models of data (pseudorandomness, locality, diffuse adversaries, etc.); average-case analysis; robust distributional analysis; resource augmentation; planted and semi-random graph models. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Prerequisites: CS161 (required). CS261 is recommended but not required.
Terms: Aut | Units: 3

CS 265: Randomized Algorithms and Probabilistic Analysis (CME 309)

Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.
Terms: Aut | Units: 3
Instructors: Valiant, G. (PI)

CS 266: Parameterized Algorithms and Complexity

An introduction to the area of parameterized algorithms and complexity, which explores multidimensional methods for measuring the difficulty and feasibility of solving computational problems. Topics include: fixed-parameter tractability (FPT) and its characterizations, FPT algorithms for hard problems, the W-hierarchy (W[1], W[2], W[P], and complete problems for these classes), and the relationships between parameterized questions and classical theory questions. Prerequisites: CS 154 and 161 or the equivalent mathematical maturity.
Terms: Aut | Units: 3
Instructors: Williams, R. (PI)

CS 267: Graph Algorithms

An introduction to advanced topics in graph algorithms. Focusing on a variety of graph problems, the course will explore topics such as small space graph data structures, approximation algorithms, dynamic algorithms, and algorithms for special graph classes. Topics include: approximation algorithms for shortest paths and graph matching, distance oracles, graph spanners, cliques and graph patterns, dynamic algorithms, graph coloring, algorithms for planar graphs. Prerequisites: 161 or the equivalent mathematical maturity.
Instructors: Williams, V. (PI)
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