This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.
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
CSE5610
FIT9131 or FIT5131 or FIT9017 or equivalent
Fundamental math with introductory knowledge of probability
Ingrid Zukerman
Consultation hours: Wed 3-4
Muhammad Amar
Andisheh Partovi
Ehsan Shareghi
Han Phan
Sadegh Kharazmi
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.
For more information on Monash’s educational strategy, see:
www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
Assessment weighting has been changed due to students' feedback.
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 to Artificial Intelligence | |
| 2 | Problem Solving I | |
| 3 | Problem Solving II | Assignment 1 handed out |
| 4 | Game playing, Knowledge representation -- logic I | |
| 5 | Knowledge representation -- logic II | Assignment 2 handed out |
| 6 | Probability and Bayesian networks I | |
| 7 | Bayesian Networks II | Assignment 1 due |
| 8 | Supervised Machine Learning I | |
| 9 | Supervised Machine Learning II | Assignment 2 due and Assignment 3 handed out |
| 10 | Unsupervised Machine Learning | |
| 11 | Topics in Language Technology I | |
| 12 | Topics in Language Technology II | Assignment 3 due |
| 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): 70%; In-semester assessment: 30%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 - Problem Solving | 12% | Week 7 |
| Assignment 2 - Knowledge representation and Bayesian Networks | 9% | Week 9 |
| Assignment 3 - Machine Learning | 9% | Week 12 |
| Examination 1 | 70% | 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/
Correctness and completeness of answers to problems.
Correctness and completeness of submitted answers and/or Bayesian networks.
Correctness and completeness of answers to machine learning problems.
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.
See Library Guides for Citing and Referencing at http://guides.lib.monash.edu/content.php?pid=88267&sid=656564
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.
Netica (free)
Netlogo (free)
Weka Data Mining Toolkit (free)
Web access
Limited copies of prescribed texts are available for you to borrow in the library.
Russell, S and Norvig, P. (2010). Artificial Intelligence - A Modern Approach. (3rd Edition) Prentice-Hall.
Witten, I and Frank, E. (2005). Data Mining - Practical Machine Learning Tools and Techniques. (3rd Edition) Elsevier.
Korb, K and Nicholson, A. (2011). Bayesian Artificial Intelligence. (2nd Edition) CRC Press.
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
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/.