Undergraduate Course: Informatics 2B - Algorithms, Data Structures, Learning (INFR08009)
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 08 (Year 2 Undergraduate) |
Credits |
20 |
Home subject area |
Informatics |
Other subject area |
None |
Course website |
http://www.inf.ed.ac.uk/teaching/courses/inf2b |
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Course description |
This 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. |
Course Delivery Information
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Delivery period: 2010/11 Semester 2, Available to all students (SV1)
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WebCT enabled: No |
Quota: None |
Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Central | Lecture | | 1-11 | | | | | 16:10 - 17:00 | Central | Lecture | | 1-11 | | | | 16:10 - 17:00 | | Central | Lecture | | 1-11 | | 16:10 - 17:00 | | | |
First Class |
Week 1, Tuesday, 16:10 - 17:00, Zone: Central. Lecture Theatre B, David Hume Tower |
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
Assessment
Two assignments, one focusing on algorithms and data structure issues and one on learning issues.
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. |
Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
Not entered |
Contacts
Course organiser |
Dr Jacques Fleuriot
Tel: (0131 6)50 9342
Email: Jacques.Fleuriot@ed.ac.uk |
Course secretary |
Ms Kendal Reid
Tel: (0131 6)50 5194
Email: kr@inf.ed.ac.uk |
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copyright 2010 The University of Edinburgh -
1 September 2010 6:09 am
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