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FIT3080 Intelligent systems - Semester 2, 2013

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).

Mode of Delivery

  • Clayton (Day)
  • Sunway (Day)

Contact Hours

2 hrs lectures/wk, 1 hr laboratory/wk

Workload requirements

The expected weekly workload is 12 hours in total, including:

  • 2 hour lecture
  • 1 hour tutorial and
  • 9 hours for personal study including programming, reading and revision.

Unit Relationships

Prohibitions

CSE2309, CSE3309, DGS3691

Prerequisites

FIT2004 or CSE2304

Chief Examiner

Campus Lecturer

Clayton

Reza Haffari

Consultation hours: Tuesday 2-3pm

Ingrid Zukerman

Consultation hours: Wednesday 3-4pm

Sunway

Simon Egerton

Tutors

Clayton

Tatyana Shmanina

Jessie Phuong Thao Nghiem

Academic Overview

Learning Outcomes

At the completion of this unit students will have -A knowledge and understanding of:
  • the historical and conceptual development of AI;
  • the goals of AI and the main paradigms for achieving them including logical inference, search, nonmonotonic logics, neural network methods and Bayesian inference;
  • the social and economic roles of AI;
  • heuristic AI for problem solving;
  • basic knowledge representation and reasoning mechanisms;
  • automated planning and decision-making systems;
  • probabilistic inference for reasoning under uncertainty;
  • machine learning techniques and their uses;
  • foundational issues for AI, including the frame problem and the Turing test;
  • AI programming techniques.
Developed attitudes that enable them to:
  • appreciate the potential and limits of the main approaches to AI;
  • be ready to reason critically about claims of the effectiveness of AI programs;
  • analyse problems and determine where AI techniques are applicable;
  • implement AI problem-solving techniques in Lisp;
  • compare AI techniques in terms of complexity, soundness and completeness.

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 Planning Assignment 1 due 2 September 2013
7 Reasoning under uncertainty: probabilistic reasoning and Bayesian networks  
8 Reasoning under uncertainty: Statistical learning  
9 Machine learning Assignment 2 due 23 September 2013
10 Classification and regression  
11 Markov Decision Processes  
12 Reinforcement Learning Assignment 3 due 21 October 2013
  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.

Assessment Summary

Examination (3 hours): 60%; In-semester assessment: 40%

Assessment Task Value Due Date
Assignment 1 - Problem solving: search 15% 2 September 2013
Assignment 2 - Knowledge representation and Bayesian networks 10% 23 September 2013
Assignment 3 - Machine learning and Markov Decision Processes 15% 21 October 2013
Examination 1 60% To be advised

Teaching Approach

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

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

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.

    Due date:
    2 September 2013
  • 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.

    Due date:
    23 September 2013
  • 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.
    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:
    21 October 2013

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
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

Assignment submission

It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/plagiarism-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.

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

Graduate Attributes Policy

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.

Your feedback to Us

Previous Student Evaluations of this Unit

Previous student feedback has been generally very positive. There is room for improvement in the provision of feedback to students.

If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp