Module Title:   Artificial Intelligence with Applications

Module Credit:   20

Module Code:   CM-1044D

Academic Year:   2015/6

Teaching Period:   PG Computing Summer Semester

Module Occurrence:   A

Module Level:   FHEQ Level 7

Module Type:   Standard module

Provider:   Computer Science

Related Department/Subject Area:   SCIM (Dept of Computer Science)

Principal Co-ordinator:   Dr Daniel Neagu

Additional Tutor(s):   Prof Peter Cowling, Dr Attila Csenki, Dr Keshav Dahal, Dr Yonghong Peng

Prerequisite(s):   None

Corequisite(s):   None

To introduce AI fundamentals and applications.
To provide an introduction to definitions of human and artificial intelligence and a study of various AI techniques: inductive logic programming, hyperheuristics, evolutionary systems, fuzzy inference systems, machine learning, neural/hybrid systems, and data mining.
To analyse each technique`s characteristics, strengths and applications.
To implement simple intelligent systems applied to specific real-life problems.
To develop abilities to build simple versions of AI applications and familiarity with full-scale versions of AI applications.

Learning Teaching & Assessment Strategy:
Lectures, laboratory/practical classes and seminars/tutorials. Concepts, principles and theories explored in formal lectures, practisesd in seminars/tutorials and demonstrated in supervised laboratory work; practical skills developed in laboratory sessions. Case studies will be used to illustrate the application of the introduced AI concepts and techniques.

Lectures:   24.00          Directed Study:   150.00           
Seminars/Tutorials:   12.00          Other:   0.00           
Laboratory/Practical:   12.00          Formal Exams:   2.00          Total:   200.00

On successful completion of this module you will be able to...

Mastery of fundamental ideas and techniques of AI

On successful completion of this module you will be able to...

Ability to critically examine and appreciate the central issues in the main sub-areas of AI and ability to apply AI techniques.

On successful completion of this module you will be able to...

Practical skills to apply basic theoretical concepts to design and implementation of simple intelligent systems.

  Coursework   50%
  1500 word report based on laboratory and tutorial exercises and original implementation/program development/test cases
  Examination - closed book 2.00 50%
  Closed book examination
  Examination - closed book 2.00 100%
  Supplementary assessment: Exam

Outline Syllabus:
1. Introduction to AI. Philosophy and History of AI. What is Intelligence? Biologically-inspired AI. Open Issues in AI.
2. Knowledge-based Systems: Knowledge representation. Inferences. Inductive logic programming. Symbolic AI. An introduction to PROLOG programming.
3. Fuzzy Sets and Systems: Fuzzy Sets. Fuzzy Variables. Fuzzy Implications and Reasoning. Fuzzy Inference Systems.
4. Hyperheuristics. Optimisation. Models, Scheduling. Local search heuristics. Constructive heuristics. Hyperheuristics. Forecasting.
5. Evolutionary computation techniques (mainly focusing on Genetic Algorithms and Genetic Programming): Introduction to Evolutionary Computational techniques. Genetic Algorithms and Genetic Progrmming. Genetic operators. Implementations. Applications. GA/GP sample code. Exercises.
6. Machine Learning (mainly focusing on Neural Networks and Neuro-Fuzzy Networks): Artificial Neural Networks. Perceptron Learning Rule. Sigmoid functions. Back-propagation. Supervised Learning. Sample code for Neural Networks. Neural Net Exercise: Pattern recognition. Neuro-Fuzzy Systems.
7. Hybrid Intelligent Systems: Comparison of Artificial neural networks, Fuzzy Inference Systems and Genetic Algorithms. Modular Connectionist Systems. Soft Computing techniques. Applications of hybrid intelligent systems - an exercise.
8. Data mining&knowledge discovery.Concepts in data mining&knowledge discovery;various data mining techniques&algorithms,the use of data mining concepts in real world applications.Some advanced data mining topics.Data mining ex

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