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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Informatics 2B - Algorithms, Data Structures, Learning (INFR08009)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) Credits20
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionThis course presents key symbolic and numerical data structures and algorithms for manipulating them. Introductory numerical and symbolic learning methods provide a context for the algorithms and data structures. To make the presented ideas concrete, the module will extend the student's skills in Java and Matlab. Examples will be taken from all areas of Informatics.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Informatics 1 - Computation and Logic (INFR08012) AND Informatics 1 - Data and Analysis (INFR08015) AND Informatics 1 - Functional Programming (INFR08013) AND Informatics 1 - Object-Oriented Programming (INFR08014) AND Introduction to Linear Algebra (MATH08057)
Prohibited Combinations Other requirements A background in calculus(differentiation of simple functions) is also required.
Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 13/01/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Seminar/Tutorial Hours 9, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 155 )
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
Summary of Intended Learning Outcomes
1 - Demonstrate the ability to analyse the complexity of algorithms using asymptotic notation.
2 - Demonstrate the ability to write programs to create and manipulate array-structured and dynamically-structured data.
3 - Demonstrate the ability to construct and analyse search tree data structures.
4 - Demonstrate knowledge of sorting algorithms and their run-time complexity
5 - Demonstrate knowledge of graph algorithms
6 - Demonstrate understanding of statistical pattern recognition and Bayes theorem
7 - Demonstrate the ability to manipulate and describe multidimensional data using summary statistics.
8 - Demonstrate the ability to model discrete multidimensional data using Naive Bayes
9 - Demonstrate the ability to classify multidimensional data using Gaussians and single-layer networks
10 - Demonstrate understanding of the concept of discriminant functions
11 - Demonstrate the ability to model data using nearest-neighbour and clustering approaches
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0

In order to pass the course you must satisfy all of the following requirements:
* achieve at least 35% in the examination;
* achieve a total of at least 25% in assessed coursework;
* obtain a combined total mark of at least 40%

Two assignments, one focusing on algorithms and data structure issues and one on learning issues.

Special Arrangements
Additional Information
Academic description Not entered
Syllabus Algorithms and Data Structures:
* Asymptotic notation and algorithms
* Sequential data structures
* Searching - including Hashing, AVL Trees, Heaps
* Sorting - including Mergesort, Heapsort, Quicksort
* Web-scale algorithms
* Graphs

* Statistical pattern recognition and machine learning
* Multidimensional data
* Discrete data and naive Bayes
* Modelling and describing continuous data: nearest neighbours and clustering
* Gaussians and linear discriminants
* Single- and multi-layer networks

Relevant QAA Computing Curriculum Sections: Data Structures and Algorithms, Artificial Intelligence
Transferable skills Not entered
Reading list * [***] S. Russell, P. Norvig. AI: A Modern Approach. Prentice Hall, 2003. 2nd Edition
* [**] T. H. Cormen, C. E. Leiserson, R. L. Rivest. Introduction to Algorithms, MIT Press, 1990.
* [**] A. V. Aho, J. D. Ullman. Foundations of Computer Science with C. Computer Science Press, 1995.
* [**] M. T. Goodrich and R. Tamassia. Data Structures and Algorithms in Java. John Wiley, 2003. (3rd edition)
* [**] I. H. Witten and E. Frank. Data Mining. Morgan Kaufmann. 2005. (2nd edition)
Programme Collective Intelligence, Toby Segaran, O'Reilly 2007
Study Abroad Not entered
Study Pattern Lectures 30
Tutorials 9
Timetabled Laboratories 0
Non-timetabled assessed assignments 50
Private Study/Other 111
Total 200
KeywordsNot entered
Course organiserProf Colin Stirling
Tel: (0131 6)50 5186
Course secretaryMs Kendal Reid
Tel: (0131 6)50 5194
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