Note: This unit version is currently under review and is subject to change!
COMP3308: Introduction to Artificial Intelligence (2019 - Semester 1)
| Unit: | COMP3308: Introduction to Artificial Intelligence (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: |
| 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: |
|
||||||||||||||||||||
| T&L Activities: | Independent Study: Students are expected to undertake the prescribed reading and work on homework exercises and assignments. |
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, machine learning and probabilistic reasoning 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 neural 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: |
|
||||||||||||||||||||||||||||||||||||
| 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: |
|
||||||||||||||||||||||||||||||||||||
| 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 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 | Statistical-based learning. |
| Evaluating and comparing classifiers. | |
| 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 Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| (7) Project and Team Skills (Level 1) | No | 0% |
| (8) Professional Effectiveness and Ethical Conduct (Level 3) | No | 0% |
| (6) Communication and Inquiry/ Research (Level 3) | No | 4% |
| (5) Interdisciplinary, Inclusiveness, Influence (Level 3) | No | 0% |
| (3) Problem Solving and Inventiveness (Level 3) | No | 13% |
| (4) Design (Level 3) | No | 0% |
| (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.
