2013-2014 2014-2015 2015-2016 2016-2017 2017-2018
Browse
by subject...
    Schedule
view...
 
  Are you a Computer Science Student? Want to make Stanford's systems even better?
Do you want to help improve the Stanford systems that you and your friends use all the time? We are looking for students interested in hacking on ExploreCourses and other upcoming university systems. Click here to learn more!

1 - 4 of 4 results for: CS 161: Design and Analysis of Algorithms

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 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 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

IMMUNOL 208: Advanced Computational and Systems Immunology

Focus is on first principles and methods of advanced computational and systems immunology that are used in the analysis of protein and nucleic acid sequences, protein structures, and immunological processes. Students learn to write computational algorithms for sequence alignment, motif finding, expression array analysis, structural modeling, structure design and prediction, and network analysis and modeling. Students become familiar with the technologies used in CSI, which include dynamic programming, Markov and hidden Markov models, Bayesian networks, clustering methods, and energy minimization approaches. Designed for students with strong foundations in either immunology or computer science . Prerequisites: lmmunol 207, CS 109 and CS 161.
Terms: not given this year | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)
Filter Results:
term offered
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints