Postgraduate Course: Randomness and Computation (INFR11089)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://course.inf.ed.ac.uk/rc |
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).
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Algorithms and Data Structures (INFR09006)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
A mathematical course with no programming.
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-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2013/14 Semester 2, Available to all students (SV1)
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Learn enabled: No |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
13/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| 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)
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| 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 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: Katey.Lee@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 13 January 2014 4:28 am
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