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1 - 10 of 14 results for: cs 161

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

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 | Grading: Letter or Credit/No Credit

CS 161: Design and Analysis of Algorithms

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR | Grading: Letter or Credit/No Credit

CS 194: Software Project

Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes a detailed proposal. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeat for credit. Prerequisites: CS 110 and CS 161.
Terms: Win, Spr | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)

CS 233: Geometric and Topological Data Analysis (CME 251)

Mathematical computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for geometric data analysis. Non-linear dimensionality reduction. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps.Networks of data sets and joint analysis for segmentation and labeling. The emergence of abstractions or concepts from data. Prerequisites: discrete algorithms at the level of 161; linear algebra at the level of CME103.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 245: Database Systems Principles

File organization and access, buffer management, performance analysis, and storage management. Database system architecture, query optimization, transaction management, recovery, concurrency control. Reliability, protection, and integrity. Design and management issues. Prerequisites: 145, 161.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Bailis, P. (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: Win | Units: 3 | Grading: Letter or Credit/No Credit

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 | Grading: Letter or Credit/No Credit

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.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 268: Geometric Algorithms

Techniques for design and analysis of efficient geometric algorithms for objects in 2-, 3-, and higher dimensions. Topics: convexity, triangulations and simplicial complexes, sweeping, partitioning, and point location. Voronoi/Delaunay diagrams and their properties. Arrangements of curves and surfaces. Intersection and visibility problems. Geometric searching and optimization. Random sampling methods. Range searching. Impact of numerical issues in geometric computation. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, and model-based recognition in computer vision. Prerequisite: discrete algorithms at the level of 161. Recommended: 164.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 276: Information Retrieval and Web Search (LINGUIST 286)

Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. Prerequisites: CS 107, CS 109, CS 161.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
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