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FIT3080 Intelligent systems - Semester 2, 2015

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.

Mode of Delivery

  • Clayton (Day)
  • Malaysia (Day)

Workload Requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 1-hour laboratory

(b.) Additional requirements (all students):

  • A minimum of 9 hours independent study per week for completing lab and project work, private study and revision.

See also Unit timetable information

Unit Relationships

Prohibitions

CSE2309, CSE3309, DGS3691

Prerequisites

FIT2004 or CSE2304

Chief Examiner

Campus Lecturer

Clayton

Reza Haffari

Ingrid Zukerman

Malaysia

Tang Tiong Yiew

Tutors

Clayton

To be announced

Your feedback to Us

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

Previous Student Evaluations of this Unit

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

Academic Overview

Learning Outcomes

At the completion of this unit, students should be able to:
  1. describe the historical and conceptual development of AI; foundational issues for AI, including the frame problem and the Turing test;
  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;
  3. explain the social and economic roles of AI;
  4. describe, analyse, apply and evaluate heuristic AI for problem solving;
  5. describe, analyse and apply basic knowledge representation and reasoning mechanisms;
  6. describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;
  7. describe, analyse, apply and evaluate machine learning techniques;
  8. describe, analyse, apply and evaluate the use of the above techniques in different domain, specifically language technology.

Unit Schedule

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.

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach provides facilitated learning, practical exploration and peer learning.

Assessment Summary

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

Assessment Requirements

Assessment Policy

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Assignment 1 - Problem solving: search
    Description:
    Implement a search algorithm to solve a given problem.
    Weighting:
    15%
    Criteria for assessment:

    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.

    Due date:
    4 September 2015
  • Assessment task 2
    Title:
    Assignment 2 - Knowledge representation and Bayesian networks
    Description:
    Pen and paper questions in knowledge representation and use of Netica for Bayesian networks.
    Weighting:
    10%
    Criteria for assessment:

    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.

    Due date:
    25 September 2015
  • Assessment task 3
    Title:
    Assignment 3 - Machine learning and Markov Decision Processes
    Description:
    Implement a program to apply machine learning techniques. The Markov Decision Process component may be pen and paper.

    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.
    7. describe, analyse, apply and evaluate machine learning techniques.
    Weighting:
    15%
    Criteria for assessment:

    Performance of the program. The specific tasks and marking criteria will be distributed at the appropriate time during the semester.

    Due date:
    23 October 2015

Examinations

  • Examination 1
    Weighting:
    60%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    None

Learning resources

Reading list

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

Feedback to you

Types of feedback you can expect to receive in this unit are:

  • Informal feedback on progress in labs/tutes
  • Graded assignments with comments
  • Graded assignments without comments
  • Solutions to tutes, labs and assignments

Extensions and penalties

Returning assignments

Resubmission of assignments

No resubmissions allowed.

Assignment submission

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.

Online submission

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.

Required Resources

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

Prescribed text(s)

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.

Other Information

Policies

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

Faculty resources and policies

Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/

Graduate Attributes Policy

Student Charter

Student services

Monash University Library

Disability Liaison Unit

Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis.