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COMP3608: Introduction to Artificial Intelligence (Adv) (2019 - Semester 1)

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Unit: COMP3608: Introduction to Artificial Intelligence (Adv) (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior
Faculty/School: School of Computer Science
Unit Coordinator/s: A/Prof Koprinska, Irena
Session options: Semester 1
Versions for this Unit:
Site(s) for this Unit: http://www.it.usyd.edu.au/~comp3308/
Campus: Camperdown/Darlington
Pre-Requisites: Distinction-level results in some 2nd year COMP or MATH or SOFT units
Prohibitions: COMP3308.
Brief Handbook Description: An advanced alternative to COMP3308; covers material at an advanced and challenging level.
Assumed Knowledge: Algorithms. Programming skills (e.g. Java, Python, C, C++, Matlab)
Additional Notes: COMP3308 and COMP3608 share the same lectures, but have different tutorials and assessment (the same type but more challenging).
Lecturer/s: A/Prof Koprinska, Irena
Timetable: COMP3608 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Independent Study 9.00 13
2 Lecture 2.00 1 13
3 Tutorial 1.00 1 12

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.

(6) Communication and Inquiry/ Research (Level 3)
1. Present and interpret data and information in verbal and written form.
(3) Problem Solving and Inventiveness (Level 3)
2. Gain practical experience in designing, implementing and evaluating search, game playing and machine learning algorithms.
(2) Engineering/ IT Specialisation (Level 3)
3. Formulate problem space description, select and apply suitable search algorithms (brute-force and heuristic) and analyse the issues involved.
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 networks, support vector machines, ensembles of classifiers) and clustering.
6. Understand probabilistic reasoning with graphical models (Bayesian networks).
7. 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.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Homeworks No 3.00 Multiple Weeks 3, 4, 5, 6, 7,
2 Quiz* No 12.00 Week 5 3,
3 Assignment 1* No 10.00 Week 6 2, 4,
4 Assignment 2* Yes 20.00 Week 10 1, 2, 5,
5 Final Exam No 55.00 Exam Period 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.

Assessments during the semester: for the exact submission times and late penalties, please see the unit outline in Canvas.

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 Computer Science 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 Computer Science 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 outline: http://www.it.usyd.edu.au/~comp3308/

Unit website on eLearning (Blackboard): This is the main website for this course. It will be used to post detailed lecture notes and tutorial notes with solutions and the assignment specifications and submission instructions. We will use the discussion board Piazza, 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.
Assessment Due: Assignment 1*
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.
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/Bachelor of Commerce 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019
Bachelor of Advanced Computing (Software Development) 2018, 2019
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
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, 2019
Biomedical 2016, 2017, 2018, 2019
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, 2019
Software/ Project Management 2019
Software 2015, 2016, 2017, 2018, 2019
Software / Arts 2016, 2017, 2018, 2019
Software / Commerce 2016, 2017, 2018, 2019
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
Software / Project Management 2016, 2017, 2018
Software / Science 2016, 2017, 2018, 2019
Software/Science (Health) 2018, 2019
Software / Law 2016, 2017, 2018, 2019
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
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of 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
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017
Software/Science (Medical Science Stream) 2018, 2019
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
(6) Communication and Inquiry/ Research (Level 3) No 4%
(7) Project and Team Skills (Level 1) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 3) No 0%
(4) Design (Level 3) No 0%
(3) Problem Solving and Inventiveness (Level 3) No 13%
(2) Engineering/ IT Specialisation (Level 3) No 83%

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.