# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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DRPS : Course Catalogue : School of Informatics : Informatics

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

 School School of Informatics College College of Science and Engineering Credit level (Normal year taken) SCQF Level 8 (Year 2 Undergraduate) Availability Available to all students SCQF Credits 20 ECTS Credits 10 Summary 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 description 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
 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) Co-requisites Prohibited Combinations Other requirements A background in calculus(differentiation of simple functions) is also required.
 Pre-requisites None High Demand Course? Yes
 Academic year 2015/16, Available to all students (SV1) Quota:  None Course Start Semester 2 Timetable Timetable 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 ) Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 % Additional Information (Assessment) 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. You should expect to spend approximately 50 hours on the coursework for this course. 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
 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
 * [***] 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
 Course URL http://course.inf.ed.ac.uk/inf2b Graduate Attributes and Skills Not entered Keywords Not entered
 Course organiser Dr Kyriakos Kalorkoti Tel: (0131 6)50 5149 Email: kk@inf.ed.ac.uk Course secretary Ms Kendal Reid Tel: (0131 6)50 5194 Email: kr@inf.ed.ac.uk
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