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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Randomness and Computation (INFR11089)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://www.inf.ed.ac.uk/teaching/courses/rc/ Taught in Gaelic?No
Course descriptionThis 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)
Pre-requisites It is RECOMMENDED that students have passed Algorithms and Data Structures (INFR09006)
Co-requisites
Prohibited Combinations 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
Pre-requisitesNone
Displayed in Visiting Students Prospectus?No
Course Delivery Information
Delivery period: 2012/13 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 14:00 - 15:50
First Class Week 1, Tuesday, 14:10 - 16:00, Zone: Central. B1, Forrest Hill
Exam Information
Exam Diet Paper Name Hours:Minutes
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.
Assessment Information
Written Examination: 70%
Assessed Assignments: 30%
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus - 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)
Transferable skills Not entered
Reading list Probability and Computing: Randomized Algorithms and Probabilistic Analysis, by Michael Mitzenmacher and Eli Upfal. (Required)

Randomized Algorithms, by Rajeev Motwani and Prabhakar Raghavan. (Useful)
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Coursework Assessed for Credit 30
Other Coursework / Private Study 50
Total 100
KeywordsNot entered
Contacts
Course organiserDr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
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© Copyright 2012 The University of Edinburgh - 14 January 2013 4:10 am