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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014
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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Algorithms and Data Structures (INFR09006)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://course.inf.ed.ac.uk/ads Taught in Gaelic?No
Course descriptionThe course aims to provide general techniques for the design of efficient algorithms and, in parallel, develop appropriate mathematical tools for analysing their performance. In this, it broadens and deepens the study of algorithms and data structures initiated in INF2. The focus is on algorithms, more than data structures. Along the way, problem solving skills are exercised and developed.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Informatics 2B - Algorithms, Data Structures, Learning (INFR08009) AND Probability with Applications (MATH08067) AND Discrete Mathematics and Mathematical Reasoning (INFR08023)
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.

Joint honours students (or Maths students) who took different second-year Maths courses should get permission of the Course lecturer

Students who did not take Inf2B should get special permission from the course lecturer.

This course has the following mathematics prerequisites:

1 - Calculus: limits, sums, integration, differentiation, recurrence relations, the Master theorem.
2 - Graph theory: graphs, digraphs, components, trees, weighted graphs, DFS, BFS.
3 - Probability: random variables, expectation, variance, Markov's inequality, Chebychev's inequality
4 - Linear algebra: vectors, matrices, matrix multiplication, scalar products.
5 - Complex numbers: the imaginary unit i, addition and multiplication in C, exponentiation.
6 - Generalities: induction, O-notation, proof by contradiction.
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2013/14 Semester 1, Available to all students (SV1) Learn enabled:  No Quota:  None
Web Timetable Web Timetable
Course Start Date 16/09/2013
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Resit Exam Diet (August)2:00
Delivery period: 2013/14 Semester 1, Part-year visiting students only (VV1) Learn enabled:  No Quota:  None
Web Timetable Web Timetable
Course Start Date 16/09/2013
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Summary of Intended Learning Outcomes
1 - Should be able to describe, and implement, the major algorithms for well known combinatorial problems such as Sorting, Matrix Multiplication, Minimum Spanning Trees, and other problems listed in the syllabus.
2 - Should be able to demonstrate familiarity with algorithmic strategies such as Divide-and-Conquer, the Greedy strategy and Dynamic Programming; and should be able to test these strategies on new problems and identify whether or not they are likely to be useful for those problems.
3 - Should be able to construct clear and rigorous arguments to prove correctness/running-time bounds of algorithms, and should be able to present these arguments in writing.
4 - Should be able to explain the importance of the data structures used in a particular implementation of an algorithm, and how the data structure that is used can affect the running time.
5 - Should be able to construct simple lower bound arguments for algorithmic problems, and to understand the relationship between upper and lower bounds. Also should be able to perform simple average-case analyses of the running-time of an algorithm, as well as worst-case analyses.
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0

Assessment
In addition to the written exam there are two pieces of assessed coursework, which involve pen and paper exercises and programming.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Introductory concepts
Review of CS2. Models of computation; time and space complexity; upper and lower bounds, big-O and big-Omega notation; average and worst case analysis.

Divide and conquer
Matrix multiplication: Strassen's algorithm; the discrete Fourier transform (DFT), the fast Fourier transform (FFT). Expressing the runtime of a recursive algorithm as a recurrence relation; solving recurrence relations.

Sorting
Quicksort and its analysis; worst-case, best-case and average-case.

Data structures: Disjoint sets
The ``disjoint sets'' (union-find) abstract data type: specification and implementations as lists and trees. Union-by-rank, path-compression, etc., ``heuristics''. Applications to finding minimum spanning trees.

Dynamic programming
Introduction to the technique; examples: Matrix-chain multiplication, Longest common subsequences.

Graph/Network algorithms
Network flow, Max-flow/min-cut theorem, Ford-Fulkerson algorithm.

Geometric algorithms
Convex hull of a set of points (in 2-d).

Relevant QAA Computing Curriculum Sections: Data Structures and Algorithms
Transferable skills Rigorous mathematics reasoning
Reading list Introduction to Algorithms (3rd Edition), Cormen, Leiserson, Rivest, Stein: . MIT Press, 2002. (Course text)


Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 8
Timetabled Laboratories 0
Non-timetabled assessed assignments 25
Private Study/Other 47
Total 100
KeywordsNot entered
Contacts
Course organiserMr Vijayanand Nagarajan
Tel: (0131 6)51 3440
Email: vijay.nagarajan@ed.ac.uk
Course secretaryMiss Claire Edminson
Tel: (0131 6)51 7607
Email: C.Edminson@ed.ac.uk
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