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 8 (Year 2 Undergraduate) |
Credits | 20 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/inf2b |
Taught in Gaelic? | No |
| 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. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2012/13 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 |
First class information not currently available |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
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| 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%
Assessment
Two assignments, one focusing on algorithms and data structure issues and one on learning issues.
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Special Arrangements
| None |
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
Learning:
* 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)
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| Study Abroad |
Not entered |
| Study Pattern |
Lectures 30
Tutorials 9
Timetabled Laboratories 0
Non-timetabled assessed assignments 50
Private Study/Other 111
Total 200 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Guido Sanguinetti
Tel: (0131 6)50 5136
Email: |
Course secretary | Ms Kendal Reid
Tel: (0131 6)50 5194
Email: |
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