Note: This unit version is currently being edited and is subject to change!

COMP3308: Introduction to Artificial Intelligence (2018 - Semester 1)

Download UoS Outline

Unit: COMP3308: Introduction to Artificial Intelligence (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior
Faculty/School: School of Information Technologies
Unit Coordinator/s: A/Prof Koprinska, Irena
Session options: Semester 1
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Prohibitions: COMP3608.
Brief Handbook Description: Artificial Intelligence (AI) is all about programming computers to perform tasks normally associated with intelligent behaviour. Classical AI programs have played games, proved theorems, discovered patterns in data, planned complex assembly sequences and so on. This unit of study will introduce representations, techniques and architectures used to build intelligent systems. It will explore selected topics such as heuristic search, game playing, machine learning, neural networks and probabilistic reasoning. Students who complete it will have an understanding of some of the fundamental methods and algorithms of AI, and an appreciation of how they can be applied to interesting problems. The unit will involve a practical component in which some simple problems are solved using AI techniques.
Assumed Knowledge: Algorithms. Programming skills (e.g. Java, Python, C, C++, Matlab)
Lecturer/s: A/Prof Koprinska, Irena
Timetable: COMP3308 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Tutorial 1.00 1
2 Independent Study 9.00 13
3 Lecture 2.00 1 13
T&L Activities: Independent Study: Students are expected to undertake the prescribed reading and work on homework exercises and assignments.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Students will learn how to formulate and solve problems as tree/graph search, game tree search, and how to formulate and solve classification, clustering and probabilistic reasoning problems. They will also gain practical experience in designing and implementing computer programs for solving such tasks. Design (Level 3)
Students will learn some of the fundamental and advanced methods and algorithms used in AI and will gain practical experience in how they can be applied to solve practical problems. Engineering/IT Specialisation (Level 3)
This is a senior Computer Science UoS. Students learn how to use software tools for data analysis and further develop proficiency in software development. Information Seeking (Level 3)
At least one of the assignments require a written report, in addition to a computer program. Communication (Level 3)
At least one of the assignments can be done in pairs (or individually). If done in pairs it will develop some basic teamwork and project management skills. Project and Team Skills (Level 1)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Design (Level 3)
1. Formulate problem space description, select and apply suitable search algorithms (brute-force and heuristic) and analyse the issues involved
2. Gain practical experience in designing, implementing and evaluating search, game playing, machine learning and probabilistic reasoning algorithms
Engineering/IT Specialisation (Level 3)
3. Appreciate some of the main ideas and views in AI, achievements and shortcomings of AI and the links between AI and other Computer Science areas
4. Understand and apply minimax search and alpha-beta pruning in game playing
5. Understand the basic principles and analyse the strengths, weaknesses and applicability of some of the main machine learning and neural network algorithms for classification (e.g. rule-based, nearest neighbor, decision tree, probabilistic, backpropagation and deep neural networks, support vector machines, ensembles of classifiers) and clustering
6. Understand probabilistic reasoning with graphical models (Bayesian networks)
7. Approach current research at the School of IT and develop interest in further studies
Communication (Level 3)
8. Present and interpret data and information in verbal and written form
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Homeworks No 3.00 Multiple Weeks 1, 3, 4, 5,
2 Quiz* Yes 12.00 Week 5 1,
3 Assignment 1* No 10.00 Mid-Semester Break 2, 4,
4 Assignment 2* Yes 20.00 Week 10 2, 5, 8,
5 Final Exam No 55.00 Exam Period 2, 4, 5, 6,
Assessment Description: Description:

1. Homeworks: Weekly homework exercises.

2. Quiz: Done in class, during the tutorials.

3. Assignment 1: Writing a computer program to solve a given task using AI algorithms.

4. Assignment 2: Writing a computer program to solve a given task using AI algorithms and also writing a report summarizing and evaluating the results. Can be done individually or in pairs.

5. Final Exam: Exam during the exam period.

No late submission is allowed for any of the assessment during the semester.

Assessments marked with * - If a special consideration is approved, no re-weighting is allowed, only deadline extension or alternative date.

Minimum performance criterion:

A student must achieve at least 40% in the final exam to pass this unit of study, regardless of the sum of the individual marks.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement It is a policy of the School of Information Technologies that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of IT may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
Online Course Content: Unit website on eLearning (Blackboard) - the main website for this course. It will be used to post detailed lecture slides, tutorial notes with solutions, the assignment specifications and submission instructions. We will also use the discussion board Piazza and the autograding system PASTA, which will be linked to the eLearning website.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction: administrative matters and course overview; what is AI, history and state of the art.
Week 2 Problem solving and search. Uninformed search: BFS, UCS, DFS and IDS.
Informed search 1: greedy best-first.
Week 3 Informed search 2: A*.
Local search.
Week 4 Game playing: game playing as search; deterministic, perfect information, 0-sum games: minimax, alpha-beta pruning; non-deterministic games.
Week 5 Introduction to machine learning. Instance-based learning. Rule-based methods.
Assessment Due: Quiz*
Week 6 Evaluating and comparing classifiers.
Statistical-based learning.
Week 7 Decision trees.
Week 8 Introduction to neural networks. Perceptrons. Multilayer neural networks 1.
Week 9 Multilayer neural networks 2. Deep learning.
Week 10 Support vector machines.
Ensembles of classifiers.
Assessment Due: Assignment 2*
Week 11 Probabilistic reasoning. Bayesian networks and inference in them.
Week 12 Unsupervised learning.
Week 13 Applications of AI.
Revision and preparation for the exam.
Mid-Semester Break Assessment Due: Assignment 1*
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing (Computational Data Science) 2018
Bachelor of Advanced Computing (Computer Science Major) 2018
Bachelor of Advanced Computing/Bachelor of Commerce 2018
Bachelor of Advanced Computing/Bachelor of Science 2018
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018
Bachelor of Advanced Computing (Information Systems Major) 2018
Bachelor of Advanced Computing (Software Development) 2018
Bachelor of Computer Science and Technology 2015, 2016, 2017
Bachelor of Computer Science and Technology (Advanced) 2015, 2016, 2017
Bachelor of Computer Science and Technology (Computer Science) 2014 and earlier 2009, 2010, 2011, 2012, 2013, 2014
Bachelor of Computer Science and Technology (Computer Science)(Advanced) 2014 and earlier 2013, 2014
Bachelor of Computer Science and Technology (Information Systems) 2014 and earlier 2010, 2011, 2012, 2013, 2014
Bachelor of Computer Science and Technology (Information Systems)(Advanced) 2014 and earlier 2013, 2014
Bachelor of Computer Science & Tech. Mid-Year 2016, 2017
Aeronautical Engineering / Science 2011, 2012, 2013, 2014
Aeronautical Engineering (Space) / Science 2011, 2012, 2013, 2014
Biomedical Engineering / Science 2013, 2014
Biomedical - Information Technology Major 2013, 2014, 2015
Chemical & Biomolecular Engineering / Science 2011, 2012, 2013, 2014
Civil Engineering / Science 2011, 2012, 2013, 2014
Electrical Engineering (Bioelectronics) / Science 2011, 2012
Electrical Engineering / Science 2011, 2012, 2013, 2014
Electrical Engineering (Computer) / Science 2014
Electrical Engineering (Power) / Science 2011, 2012, 2013, 2014
Electrical Engineering (Telecommunications) / Science 2011, 2012, 2013, 2014
Aeronautical / Science 2015, 2016, 2017
Aeronautical (Space) / Science 2015
Biomedical Mid-Year 2016, 2017, 2018
Biomedical 2016, 2017, 2018
Biomedical /Science 2015, 2016, 2017
Chemical & Biomolecular / Science 2015
Civil / Science 2015
Electrical / Science 2015
Electrical (Computer) / Science 2015
Electrical (Power) / Science 2015
Electrical (Telecommunications) / Science 2015
Mechanical / Science 2015, 2016, 2017
Mechanical (Space) / Science 2015
Mechatronic / Science 2015, 2016, 2017
Mechatronic (Space) / Science 2015
Software Mid-Year 2016, 2017, 2018
Software 2015, 2016, 2017, 2018
Software / Arts 2016, 2017, 2018
Software / Commerce 2016, 2017, 2018
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
Software / Project Management 2016, 2017, 2018
Software / Science 2016, 2017, 2018
Software/Science (Health) 2018
Software / Law 2016, 2017, 2018
Mechanical Engineering (Biomedical) / Science 2011, 2012
Mechanical Engineering / Science 2011, 2012, 2013, 2014
Mechanical Engineering (Space) / Science 2011, 2012, 2013, 2014
Mechatronic Engineering / Science 2011, 2012, 2013, 2014
Mechatronic Engineering (Space) / Science 2011, 2012, 2013, 2014
Project Engineering and Management (Civil) / Science 2011
Software Engineering (till 2014) 2010, 2011, 2012, 2013, 2014
Software Engineering / Arts 2011, 2012, 2013, 2014
Software Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Software Engineering / Medical Science 2011, 2012, 2013, 2014
Software Engineering / Project Management 2012, 2013, 2014
Software Engineering / Science 2011, 2012, 2013, 2014
Bachelor of Information Technology 2015, 2016, 2017
Information Technology / Arts 2015, 2016, 2017
Information Technology / Commerce 2015, 2016, 2017
Information Technology / Medical Science 2015, 2016, 2017
Information Technology / Science 2015, 2016, 2017
Bachelor of Information Technology (Computer Science) 2014 and earlier 2009, 2010, 2011, 2012, 2013, 2014
Information Technology (Computer Science)/Arts 2012, 2013, 2014
Information Technology (Computer Science) / Commerce 2012, 2013, 2014
Information Technology (Computer Science) / Medical Science 2012, 2013, 2014
Information Technology (Computer Science) / Science 2012, 2013, 2014
Information Technology (Computer Science) / Law 2012, 2013, 2014
Bachelor of Information Technology (Information Systems) 2014 and earlier 2010, 2011, 2012, 2013, 2014
Information Technology (Information Systems)/Arts 2012
Information Technology / Law 2015, 2016, 2017
Software/Science (Medical Science Stream) 2018
Information Technology (Information Systems) / Science 2012
Flexible First Year (Stream A) / Science 2012

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Design (Level 3) Yes 39.5%
Engineering/IT Specialisation (Level 3) Yes 56.5%
Information Seeking (Level 3) Yes 0%
Communication (Level 3) Yes 4%
Project and Team Skills (Level 1) Yes 0%
Professional Conduct (Level 3) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.