CS3AI18-Artificial Intelligence

Module Provider: Computer Science
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: 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 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.

  • V alue of Perfect Information and Markov Models.

  • Hidden Markov Models.

  • Naive Bayes.

  • Perceptrons.

  • Deep Learning.


Brief description of teaching and learning methods:

The module consists of lectures and tutorials throughout the term.


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
Written exam 30
Project output other than dissertation 70

Summative assessment- Examinations:

One 1.5-hour examination paper in May/June.


Summative assessment- Coursework and in-class tests:


  • One project-based assignment (70%)


Formative assessment methods:

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.


Reassessment arrangements:

One 2-hour examination paper in August/September.  Note that the resit module mark will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (30% exam, 70% coursework).


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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