CSMAI19-Artificial Intelligence

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded: CS3AI18 Artificial Intelligence
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.ÌýThis module aims to provide knowledge of artificial intelligence techniques such as problem solving, search, reasoning, learning, and perception. In this module, students will learn state-of-the-art deep learning methods.Ìý

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 algorithms and techniques to tackleÌýreal-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.Ìý

This module also encourages students to develop a set of professional skills, such as effective use of commercial software.ÌýFinally, 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.

Assessable learning outcomes:

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

• explain and describe algorithms and techniques of artificial intelligence


• apply the state-of-the-art Artificial Intelligence algorithms and methods in real-world problems.


• innovate the state-of-the-art Artificial Intelligence algorithms and methods in real-world problems.


• have knowledge of fundamentals of search and planning in AI


• have knowledge of foundation of a satisfiability problem and algorithms for Sat-solving


• have knowledge of Reinforcement Learning.


• create an Artificial Intelligence project by applying AI algorithms for Real-world problems (Games, Robotics, Synthetic Biology)

This module will be assessed to a greater depth than the excluded module CS3AI18.

Additional outcomes:

The students will become familiar with the potential applications of data artificial intelligence techniques in different domains. They will also learn how to carry out experimental tests for algorithm performance evaluations.

Outline content:

• Nature and goals of AI. Application areas


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


• A* search algorithm. Use of heuristics in search


• Constraint Satisfaction ProblemsÌý


• Game Trees


• Markov Decision ProcessesÌý


• Reinforcement Learning


• Bayes' Nets: Representation, Inference and Sampling


• Decision Networks


• Naive BayesÌý


• ±Ê±ð°ù³¦±ð±è³Ù°ù´Ç²Ô²õÌý


• Deep Learning


• Advanced Topics: Robotics


• Advanced Topics: Programming Cells and MicroorganismsÌý


• Advanced Topics: Games (e.g., ATARI games, DeepMind DQN)

Brief description of teaching and learning methods:

The module consists of lectures and tutorials.Ìý

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One piece of coursework.

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 􀀓Penalties for late submission for Postgraduate Flexible programmes􀀔, which can be found here: