COMP24011
Introduction to AI
10
Semester 1
Undergraduate
Level 2
Ian Pratt-Hartmann
Unit coordinator
1
FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors '
European Credit Transfer & Accumulation System Rating : 5.0
<p>The Unit aims to make students familiar with the basic concepts and techniques of Artificial Intelligence. It provides the knowledge and understanding that underpins later course units in the subject taught in the Department.</p>
<p>The Unit constitutes an introduction to the field of Artificial Intelligence, aiming at once to give a broad overview of the subject and to serve as a basis for more detailed third year courses, particularly, COMP34120 (AI and Games), COMP34212 (Cognitive Robotics), COMP34412 (Natural Language Systems) and COMP37212 (Computer Vision).</p><p>This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates.</p>
<p>A student completing this course should:</p><ul> <li>be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems;</li> <li>be able to apply first-order logic to model physical situations and reason about the effects of actions,</li> <li>to appreciate the limitations of logic and to select appropriate responses to these limitations;</li> <li>be able to develop formal ontologies to represent knowledge in different domains;</li> <li>be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations;</li> <li>be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics;</li> <li>be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence;</li> <li>have an appreciation of the central philosophical problems connected with artificial intelligence.</li></ul>
<div> On the successful completion of the course, students will be able to: </div><ul> <li> ILO 1<span style="white-space:pre"> </span>To be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.</li> <li> ILO 2<span style="white-space:pre"> </span>To be able to apply first-order logic to model physical situations and to reason about the effects of actions.</li> <li> ILO 3<span style="white-space:pre"> </span>To appreciate the limitations of logic and to be able to select appropriate responses to these limitations.</li> <li> ILO 4<span style="white-space:pre"> </span>To be able to develop formal ontologies to represent knowledge in different domains.</li> <li> ILO 5<span style="white-space:pre"> </span>To be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations.</li> <li> ILO 6<span style="white-space:pre"> </span>To be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.</li> <li> ILO 7<span style="white-space:pre"> </span>To be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence.</li></ul><div> </div>
<div> Topic 1. Search and planning:</div><div> problem-solving as search; adversarial games; classical planning.</div><div> Topic 2. Logic and reasoning</div><div> review of first-order logic; applications of logic to planning; logic versus reasoning; default `logic'.</div><div> Topic 3. AI and probability</div><div> review of probability theory; alternative representations of uncertainly; Bayes' networks.</div><div> Topic 4. Knowledge representation</div><div> ontology-driven database access; formal ontologies and knowledge-representation.</div><div> Topic 5. The periphery:</div><div> sensors and actuators; sensor integration, simultaneous localization and mapping.</div><div> Topic 6. Philosophical issues:</div><div> the Turing test; the meaning of `AI'; the problem of consciousness.</div>
<p>2 hours lectures per week (22 hours in total), 2 hours of lab per fortnight (8 hours in total)</p>
1
Written exam
80%
2
Written assignment (inc essay)
20%
<p>Exam and assessments</p><div>Coursework:</div><div> </div><div>Lab 1: Games</div><div>Lab 2: Constraints </div><div>Lab 3: SLAM </div><div>Lab 4: BM25</div>
COMP11120
Mathematical Techniques for Computer Science
Pre-Requisite
Compulsory
COMP13212
Data Science
Pre-Requisite
Compulsory
Students who are not from the Department of Computer Science must have permission from both Computer Science and their home School to enrol.<p>COMP11120 (not a pre-requisite for CM)</p>
Y
<p><a href="http://studentnet.cs.manchester.ac.uk/syllabus/index.php?code=COMP24011&year=2020">COMP24011 reading list</a> can be found on the Department of Computer Science website for current students.</p><div> Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, Global Edition, Pearson, 2016</div><div> </div><div> Ronald Brachman: Knowledge representation and reasoning, Morgan Kaufmann, 2004</div><div> </div><div> Simon J. D. Prince: Computer vision : models, learning, and inference, Cambridge University Press, 2012.</div><div> </div>
Lectures
22
Practical classes & workshops
8
0
70