Contents
- Course Details
- Timetable
- Course Summary
- Assumed Knowledge
- Student Learning Outcomes
- Teaching
- Assessment
- Resources for Students
- Student Conduct
- Course Evaluation and Development
Course Details
| Course Code | COMP3411/9814 |
| Course Title | Artificial Intelligence |
| Units of Credit | 6 |
| Course Website | http://cse.unsw.edu.au/~cs3411 |
| Handbook Entry |
http://www.handbook.unsw.edu.au/undergraduate/courses/current/COMP3411.html
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|
Lecturer-in-Charge
|
Claude Sammut <cs3411@cse.unsw.edu.au> |
| Course Admin | Armin Chitizadeh <cs3411@cse.unsw.edu.au> |
Timetable
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
|
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09:00 - 10:00 |
|
|
|
|
|
|
|
10:00 - 11:00 |
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|
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Tut - H10A
Adam Stucci
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Tut
- H10B
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11:00 - 12:00 |
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|
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Tut
- H11A
(online)
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Tut
- H11B
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12:00 - 1300 |
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|
|
Tut
- H12A
(online)
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Tut
- H12B
|
|
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13:00 - 14:00 |
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|
|
Tut
- H13A
(online)
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Tut
- H13B
|
|
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14:00 - 15:00 |
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|
|
Tut
- H14A
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Tut
- H14B
(online)
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Lecture
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15:00 - 16:00 |
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|
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Tut
- H15A
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Tut
- H15B
(online)
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16:00 - 17:00 |
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Lecture
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Tut
- H16A
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Tut
- H16B
(online)
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17:00 - 18:00 |
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Tut
- H17A
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Tut
- H17B
(online)
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18:00 - 19:00 |
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|
Tut
- W18A
(online)
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Tut
- H18A
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Tut
- H18B
(online)
|
|
Course Summary
Artificial Intelligence is concerned with the design and construction of computer systems that "think". This course will introduce students to the main ideas and approaches in AI - including agent architectures, AI programming, search techniques, knowledge representation and reasoning, machine learning, natural language processing, logical inference and robotics.
This course will introduce you to the following main ideas and approaches in AI, which will be presented in four main modules:
1. Artificial Intelligence, Intelligent Agents
- Introduction and history of AI
- Agents and Autonomous Systems
2. Knowledge and Reasoning
- Path Search
- Heuristic Path Search
- Constraint Satisfaction
- Logical Agents and Logic Programming
- Uncertainty
3. Machine Learning
- Learning and Decision Trees
- Reinforcement Learning and Neural Networks
4. Additional topics:
- Natural Language Processing
- Computer Visions
- Robotics
Assumed Knowledge
COMP3411
Pre-requisite: COMP1927 or COMP2521
COMP9814
Pre-requisite: COMP9024
Student Learning Outcomes
Students successfully completing this course will:
- Gain a working knowledge of fundamental AI methods and techniques.
- Be able to demonstrate knowledge of AI methods by explaining certain features or aspects of these algorithms.
- Describe how the techniques would be applied to particular problems.
- Develop competency in the AI programming.
- Gain practical experience through the assignments to understand what is involved in designing and implementing a functional AI system.
Teaching
There will be four hours of lectures per week, plus one hour of tutorial. The major AI algorithms and learning techniques will be presented in lectures and illustrated on sample problems, along with historical background and theoretical motivation.Tutorials give students a chance to clarify the ideas mentioned in lectures and practice their problem-solving skills in a small (and hopefully more personal) class with the assistance of a tutor. Students are expected to prepare for and actively participate in tutorials. Most tutorials will also include one or two questions of a speculative nature - which can lead to more in-depth discussion of particular topics, depending on the interests of the students.
Assessment
The assessable components of the course are:
Component Mark Assignments 40% Written Exam 60%
Further details about the assignments will be posted on the Course Web site. Programming assignments give the students an opportunity to put into practice the ideas and approaches that have been presented in lectures and discussed in tutorials. They may, for example, involve writing a program to:
- enable an agent to act in a simulated environment
- solve a problem using search techniques
- play a game
- apply a machine learning algorithm
- enable communication or co-operation between agents
In order to pass the course, students must score:
- at least 16/40 for the assignments
- at least 24/60 for the exam
- a combined mark of at least 50/100
Resources for Students
The recommended textbook for this course is:- David L. Poole and Alan K. Mackworth Artificial Intelligence: Foundations of Computational Agents , 2nd Edition
There is an electronic version of the book, as well as print. Here are the links to both:
- Print - https://www.bookshop.unsw.edu.au/details.cgi?ITEMNO=9781107195394
- Digital - https://artint.info/2e/html/ArtInt2e.html
The following books might also serve as additional reference material:
- Stuart Russell and Peter Norvig, Artificial Intelligence: a Modern Approach , 3th Ed., Prentice Hall, 2010.
- Ivan Bratko, Prolog Programming for Artificial Intelligence , 4th Edition, Pearson, 2013.
- Nils J. Nilsson, Artificial Intelligence: a New Synthesis , Morgan Kaufmann, 1998, ISBN 1-55860-467-7.
- Valentino Braitenberg, Vehicles: Experiments in Synthetic Psychology , MIT Press, 1984, ISBN 0-262-52112-1.
Links to other electronic resources will be provided on the Course Web page throughout the session.
Student Conduct
The Student Code of Conduct ( Information , Policy ) sets out what the University expects from students as members of the UNSW community. As well as the learning, teaching and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.
In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code Policy as discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.
If you have any concerns, you may raise them with your lecturer, or approach the School Ethics Officer , Grievance Officer , or one of the student representatives.
Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:
Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible that your assignment files are not accessible by anyone but you by setting the correct permissions in your CSE directory and code repository, if using. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is defined as using the words or ideas of others and passing them off as your own.
If you haven't done so yet, please take the time to read the full text of
The pages below describe the policies and procedures in more detail:
Course Evaluation and Development
This course is evaluated each session using the myExperience system.
Resource created Thursday 03 February 2022, 04:11:57 PM, last modified Wednesday 16 February 2022, 04:47:05 AM.