Postgraduate Course: Randomness and Computation (INFR11089)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Informatics
||Other subject area||None
||Taught in Gaelic?||No
|Course description||This course is about probabilistic methods and their application to computer science. The course introduces basic models and techniques and applies these techniques to the design of various randomized algorithms, data structures, and distributed protocols. Special emphasis will be given on applications of these ideas to other areas of computer science (e.g. networking, machine learning, etc).
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Algorithms and Data Structures (INFR09006)
||Other requirements|| Basic knowledge of (1) discrete probability and (2) algorithms is required. In particular, the students should have a good understanding of the following concepts:
(1) probability spaces and events, conditional probability and independence, random variables, expectations and moments, conditional expectation.
(2) asymptotic notation, basic sorting algorithms (Quick-sort, Merge-sort), basic graph algorithms (BFS, DFS, Dijkstra).
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||No
Course Delivery Information
|Delivery period: 2012/13 Semester 2, Available to all students (SV1)
||Learn enabled: No
|Central||Lecture||1-11|| 14:00 - 15:50|
||Week 1, Tuesday, 14:10 - 16:00, Zone: Central. B1, Forrest Hill |
|Main Exam Diet S2 (April/May)||2:00|
Summary of Intended Learning Outcomes
|1. Apply fundamental tools in discrete probability (e.g. concentration inequalities, probabilistic method, random walks).
2. Know randomized algorithms and data structures for selected combinatorial and graph problems.
3. Be able to analyze error probabilities and expected running time of randomized algorithms.
4. Understand the fundamentals of Markov chains and their algorithmic applications.
5. Apply Monte Carlo methods such as MCMC.
|Written Examination: 70%|
Assessed Assignments: 30%
||- Introduction: Las Vegas and Monte Carlo algorithms
(Elementary Examples: checking identities, fingerprinting)
- Moments, Deviations and Tail Inequalities
(Balls and Bins, Coupon Collecting, stable marriage, routing)
- Randomization in Sequential Computation
(Data Structures, Graph Algorithms)
* Randomization in Parallel and Distributed Computation
(algebraic techniques, matching, sorting, independent sets)
* Randomization in Online Computation
(online model, adversary models, paging, k-server)
- The Probabilistic Method
(threshold phenomena in random graphs, Lovasz Local Lemma)
- Random Walks and Markov Chains
(hitting and cover times, Markov chain Monte Carlo)
||Probability and Computing: Randomized Algorithms and Probabilistic Analysis, by Michael Mitzenmacher and Eli Upfal. (Required)
Randomized Algorithms, by Rajeev Motwani and Prabhakar Raghavan. (Useful)
Timetabled Laboratories 0
Coursework Assessed for Credit 30
Other Coursework / Private Study 50
|Course organiser||Dr Iain Murray
Tel: (0131 6)51 9078
|Course secretary||Miss Kate Weston
Tel: (0131 6)50 2701
© Copyright 2012 The University of Edinburgh - 14 January 2013 4:10 am