CS3AI18-Artificial Intelligence

Module Provider: School of Mathematical, Physical and Computational Sciences
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Dr Yevgeniya Kovalchuk
Email: y.kovalchuk@reading.ac.uk

Type of module:

Summary module description:

The main goal of this module is to familiarise students with fundamental algorithms and methods in Artificial Intelligence. This module aims to provide knowledge of artificial intelligence techniques such as problems solving, search, reasoning, learning, and perception. In this module, students will learn state-of-the-art deep learning method.

The module aims to provide students with theoretical and practical knowledge of Artificial Intelligence from various techniques and applications.

Aims:

The main goal of the module is to equip students with the knowledge of Artificial Intelligence algorithms and techniques to tackle real-world problems (Artificial Intelligence applications) such as function optimisation, speech recognition, face recognition, web search, autonomous driving, automatic scheduling, autonomous systems, smart building, games, robotics. This module also encourages students to develop a set of professional skills such as the effective use of commercial software. Finally, upon successful completion of the module, students will have developed a wide range of practical skills necessary for modelling problem domains, including games, planning and robotics. Moreover, the module will provide an opportunity for students to develop their Python skills and apply that to Artificial Intelligence through practical assignments.

Assessable learning outcomes:

By the end of the module, students should be able to:

• describe the basic algorithms and techniques of artificial intelligence.


• apply state-of-the-art Artificial Intelligence algorithms and methods to real-world problems to create a small-scale AI project.


• Have knowledge of the fundamentals of search and planning.


• Have knowledge of the foundation of a satisfiability problem and algorithms for Sat-solving.


• Have knowledge of Reinforcement Learning.

Additional outcomes:

Improved programming skills and applied AI through practical work.

Outline content:

• Nature and goals of AI. Application areas.


• Searching state-spaces. Use of states and transitions to model problems.


• A* search algorithm. Use of heuristics in search.


• Constraint Satisfaction Problems.


• Game Trees.


• Markov Decision Processes.


• Reinforcement Learning.


• Bayes' Nets: Representation, Inference and Sampling.


• Decision Networks.


• N aive Bayes.


• Perceptrons.


• Deep Learning.

Brief description of teaching and learning methods:

The module consists of lectures and tutorials throughout the term.