This unit includes history and philosophy 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, situation calculus, planning, frames and semantic networks); expert systems overview (production systems, certainty factors); reasoning under uncertainty (belief networks compared to other approaches such as fuzzy logic); machine learning (decision trees, neural networks, genetic algorithms).
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
CSE2309, CSE3309, DGS3691
FIT2004 or CSE2304
Reza Haffari
Ingrid Zukerman
Simon Egerton
To be announced
Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through the Student Evaluation of Teaching and Units (SETU) survey. The University’s student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.
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Previous student feedback has been generally very positive. Improvements will be made in the provision of feedback to students.
If you wish to view how previous students rated this unit, please go to
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| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction | |
| 2 | Problem solving: search I | Quizzes at beginning of 10 tutorials between Weeks 2 and 12 |
| 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 5 September 2014 |
| 7 | Reasoning under uncertainty - Utility Theory | |
| 8 | Markov Decision Processes (MDPs) | |
| 9 | Reinforcement Learning | Assignment 2 due 26 September 2014 |
| 10 | Mathematical Principles of Machine Learning | |
| 11 | Supervised Learning: Classification and Regression | |
| 12 | Natural Language Processing | Assignment 3 due 24 October 2014 |
| 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 | 13% | 5 September 2014 |
| Assignment 2 - Knowledge representation and Bayesian networks | 9% | 26 September 2014 |
| Assignment 3 - Machine learning and Markov Decision Processes | 13% | 24 October 2014 |
| Quizzes | 5% | Beginning of 10 tutorials during Weeks 2 to 12 |
| 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.
Knowledge of the requisite material. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.
Performance of the program. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.
Knowledge of the material.
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
Faculty of Information Technology Style Guide
Examination/other end-of-semester assessment feedback may take the form of feedback classes, provision of sample answers or other group feedback after official results have been published. Please check with your lecturer on the feedback provided and take advantage of this prior to requesting individual consultations with staff. If your unit has an examination, you may request to view your examination script booklet, see http://intranet.monash.edu.au/infotech/resources/students/procedures/request-to-view-exam-scripts.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
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 online quiz). 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
Key educational policies include:
Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/
The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at http://www.monash.edu.au/students. For Malaysia see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/.
The Monash University Library provides a range of services, resources and programs that enable you to save time and be more effective in your learning and research. Go to www.lib.monash.edu.au or the library tab in my.monash portal for more information. At Malaysia, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.