printer friendly pageCS 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
Instructors:
Guibas, L. (PI)
;
Su, J. (PI)
;
Williams, V. (PI)
;
Ahmed, D. (TA)
;
Anenberg, B. (TA)
;
Au, B. (TA)
;
Chiam, J. (TA)
;
Day, C. (TA)
;
Desai, S. (TA)
;
Ekmekji, A. (TA)
;
Eng, D. (TA)
;
Hildick-Smith, S. (TA)
;
Hong, J. (TA)
;
How, P. (TA)
;
Kim, A. (TA)
;
Lee, H. (TA)
;
NeCamp, J. (TA)
;
Ostrow, R. (TA)
;
Padmanabhan, S. (TA)
;
Pham, H. (TA)
;
Shen, C. (TA)
;
Starosta, A. (TA)
;
Su, J. (TA)
;
Weiler, A. (TA)
;
Yang, A. (TA)
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
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: Aut
| Units: 3
Instructors:
Khatri, P. (PI)
;
Mallick, P. (PI)
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