Undergraduate Course: Algorithms and Data Structures (INFR10052)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 10 (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, bigO and bigOmega 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; worstcase, bestcase and averagecase.
Data structures: Disjoint sets
The ``disjoint sets'' (unionfind) abstract data type: specification and implementations as lists and trees. Unionbyrank, pathcompression, etc., ``heuristics''. Applications to finding minimum spanning trees.
Dynamic programming
Introduction to the technique; examples: Matrixchain multiplication, Longest common subsequences.
Graph/Network algorithms
Network flow, Maxflow/mincut theorem, FordFulkerson algorithm.
Geometric algorithms
Convex hull of a set of points (in 2d).
Relevant QAA Computing Curriculum Sections: Data Structures and Algorithms

Entry Requirements (not applicable to Visiting Students)
Prerequisites 
Students MUST have passed:
Informatics 2B  Algorithms, Data Structures, Learning (INFR08009) AND
Probability with Applications (MATH08067) AND
Discrete Mathematics and Mathematical Reasoning (INFR08023)

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Algorithms and Data Structures (INFR09006)

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 secondyear Maths courses should get permission of the Course lecturer
Students who did not take Informatics 2B  Algorithms and Data Structures, Learning 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, Onotation, proof by contradiction, confidence in proving theorems. 
Information for Visiting Students
Prerequisites  Visiting students are required to have comparable background to that
assumed by the course prerequisites listed in the Degree Regulations &
Programmes of Study. If in doubt, consult the course lecturer. 
Course Delivery Information

Academic year 2014/15, Available to all students (SV1)

Quota: None 
Course Start 
Semester 1 
Timetable 
Timetable 
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 )

Assessment (Further Info) 
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %

Additional Information (Assessment) 
In addition to the written exam there are two pieces of assessed coursework, which involve pen and paper exercises and programming.
You should expect to spend approximately 25 hours on the coursework for this course.
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. 
Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S2 (April/May)   2:00   Resit Exam Diet (August)   2:00  

Academic year 2014/15, Partyear visiting students only (VV1)

Quota: None 
Course Start 
Semester 1 
Timetable 
Timetable 
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 )

Assessment (Further Info) 
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %

Additional Information (Assessment) 
In addition to the written exam there are two pieces of assessed coursework, which involve pen and paper exercises and programming.
You should expect to spend approximately 25 hours on the coursework for this course.
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. 
Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S1 (December)   2:00  
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 DivideandConquer, 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/runningtime 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 averagecase analyses of the runningtime of an algorithm, as well as worstcase analyses.

Reading List
Introduction to Algorithms (3rd Edition), Cormen, Leiserson, Rivest, Stein: . MIT Press, 2002. (Course text) 
Contacts
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 

