# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2014/2015 Archive for reference only THIS PAGE IS OUT OF DATE

 University Homepage DRPS Homepage DRPS Search DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

# Undergraduate Course: Algorithms and Data Structures (INFR09006)

 School School of Informatics College College of Science and Engineering Credit level (Normal year taken) SCQF Level 9 (Year 3 Undergraduate) Availability Available to all students SCQF Credits 10 ECTS Credits 5 Summary The 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. Course description 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
 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.
 Pre-requisites None
 Not being delivered
 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.
 Introduction to Algorithms (3rd Edition), Cormen, Leiserson, Rivest, Stein: . MIT Press, 2002. (Course text)
 Course URL http://course.inf.ed.ac.uk/ads Graduate Attributes and Skills Rigorous mathematics reasoning Keywords Not entered
 Course organiser Dr Mary Cryan Tel: (0131 6)50 5153 Email: mcryan@inf.ed.ac.uk Course secretary Mrs Victoria Swann Tel: (0131 6)51 7607 Email: Vicky.Swann@ed.ac.uk
 Navigation Help & Information Home Introduction Glossary Search DPTs and Courses Regulations Regulations Degree Programmes Introduction Browse DPTs Courses Introduction Humanities and Social Science Science and Engineering Medicine and Veterinary Medicine Other Information Combined Course Timetable Prospectuses Important Information
© Copyright 2014 The University of Edinburgh - 12 January 2015 4:10 am