This unit includes history of artificial intelligence; intelligent agents; problem solving and search (problem representation, heuristic search, iterative improvement, game playing); knowledge representation and reasoning (extension of material on propositional and first-order logic for artificial intelligence applications, planning, frames and semantic networks); reasoning under uncertainty (belief networks); machine learning (decision trees, Naive Bayes, neural nets and genetic algorithms); language technology.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
See also Unit timetable information
CSE2309, CSE3309, DGS3691
FIT2004 or CSE2304
Reza Haffari
Ingrid Zukerman
Tang Tiong Yiew
To be announced
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Previous student feedback has been generally very positive. Improvements will be made in the provision of feedback to students.
The students didn't like the tutorial quizzes, which have now been cancelled.
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction | |
| 2 | Problem solving: search I | |
| 3 | Problem solving: search II | |
| 4 | Game playing and Knowledge representation: propositional logic | |
| 5 | Knowledge representation: first-order logic | |
| 6 | Reasoning under uncertainty | Assignment 1 due 4 September 2015 |
| 7 | Reasoning under uncertainty - Utility Theory | |
| 8 | Markov Decision Processes (MDPs) | |
| 9 | Reinforcement Learning | Assignment 2 due 25 September 2015 |
| 10 | Mathematical Principles of Machine Learning | |
| 11 | Supervised Learning: Classification and Regression | |
| 12 | Natural Language Processing | Assignment 3 due 23 October 2015 |
| SWOT VAC | No formal assessment is undertaken in SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your learning system.
Examination (3 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 - Problem solving: search | 15% | 4 September 2015 |
| Assignment 2 - Knowledge representation and Bayesian networks | 10% | 25 September 2015 |
| Assignment 3 - Machine learning and Markov Decision Processes | 15% | 23 October 2015 |
| Examination 1 | 60% | To be advised |
Faculty Policy - Unit Assessment Hurdles (http://intranet.monash.edu.au/infotech/resources/staff/edgov/policies/assessment-examinations/assessment-hurdles.html)
Academic Integrity - Please see resources and tutorials at http://www.monash.edu/library/skills/resources/tutorials/academic-integrity/
Students must demonstrate knowledge of the A* algorithm and other search algorithms, and ability to implement them correctly.
Group work will be optionally assessed by interview where all participants must exhibit adequate knowledge of the material.
Learning outcomes:
2. explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference.
4. describe, analyse, apply and evaluate heuristic AI for problem solving.
Knowledge of the requisite material. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.
Group work will be optionally assessed by interview where all participants must exhibit adequate knowledge of the material.
Learning outcomes:
2. explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference.
5. describe, analyse and apply basic knowledge representation and reasoning mechanisms.
6. describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty.
Performance of the program. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.
Recommended texts:
• A Hodges (1992), Alan Turing: The Enigma. London: Vintage.
• P McCorduck (1979), Machines Who Think. Freeman.
• J Haugland (1985), Artificial Intelligence: The Very Idea. MIT.
• M Boden (Ed.) (1990), The Philosophy of AI. Oxford.
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html
Types of feedback you can expect to receive in this unit are:
Submission must be made by the due date otherwise penalties will be enforced.
You must negotiate any extensions formally with your campus unit leader via the in-semester special consideration process: http://www.monash.edu.au/exams/special-consideration.html
No resubmissions allowed.
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an electronic submission). Please note that it is your responsibility to retain copies of your assessments.
If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.
Please check with your lecturer before purchasing any Required Resources. Limited copies of prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
Software: Netica, Weka
Limited copies of prescribed texts are available for you to borrow in the library.
R. Russell and P. Norvig. (2010). Artificial Intelligence: A Modern Approach. (3rd Edition) Prentice Hall.
Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and to provide advice on how they might uphold them. You can find Monash’s Education Policies at: www.policy.monash.edu.au/policy-bank/academic/education/index.html
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