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: 2020/1

Module Convenor: Dr Varun Ojha

Email: v.k.ojha@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 problem solving, search, reasoning, learning, and perception. In this module, students will learn state-of-the-art deep learning methods. 



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 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 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.


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 knoweldge 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 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

  • Value of P erfect Information and Markov Models

  • Hidden Markov Models

  • Naive Bayes

  • Perceptrons

  • Deep Learning

  • Advanced Topics: Robotics

  • Advanced Topics: Programming Cells and Microorganisms

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


Brief description of teaching and learning methods:

The module consists of lectures and tutorials. 


Contact hours:
  Autumn Spring Summer
Lectures 16
Seminars 4
Guided independent study: 80
       
Total hours by term 0 0
       
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Project output other than dissertation 100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One piece of coursework.


Formative assessment methods:

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

A mark of 50% overall.


Reassessment arrangements:

One 2-hour examination paper in August/September.


Additional Costs (specified where applicable):

Last updated: 16 April 2020

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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