COSC367-24S2 (C) Semester Two 2024

Artificial Intelligence

15 points

Details:
Start Date: Monday, 15 July 2024
End Date: Sunday, 10 November 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 28 July 2024
  • Without academic penalty (including no fee refund): Sunday, 29 September 2024

Description

This course introduces major concepts and algorithms in Artificial Intelligence. Topics include problem solving, reasoning, games, and machine learning.

Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.

In this course, you will explore a diverse array of techniques and methodologies aimed at developing computer programs that demonstrate intelligent behavior akin to that observed in humans and other animals. The curriculum covers a broad spectrum of topics that encapsulate the multifaceted nature of Artificial Intelligence (AI):

- Planning
- State-space search
- Uninformed and informed graph search algorithms
- Propositional logic and forward and backward chaining algorithms
- Knowledge representation
- Predicate logic and declarative programming
- Constraint satisfaction problems, backtracking, and consistency-based algorithms
- Optimisation, local and global search, heuristic and population-based algorithms
- Belief networks and probabilistic inference algorithms
- Machine learning (classification, regression, neural networks, learning parameters)
- Games and adversarial search (minimax, alpha-beta pruning, more advanced methods)
- Impacts of AI (ethical, societal, economical, etc.)

Learning Outcomes

  • Identify problems amenable to transformation into state-space graphs or adversarial search, and employ known algorithms to solve them.
  • Understand and implement inference algorithms in both deterministic and non-deterministic contexts.
  • Identify and resolve constraint satisfaction and optimisation problems using various techniques.
  • Understand and apply basic supervised machine learning algorithms for building predictive models.
  • Comprehend the complexities, limitations, and implications of using various AI algorithms and techniques.

Prerequisites

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Friday 09:00 - 11:00 C2 Lecture Theatre
15 Jul - 25 Aug
9 Sep - 20 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Thursday 13:00 - 15:00 Jack Erskine 136 Lab 4
22 Jul - 25 Aug
9 Sep - 20 Oct
02 Friday 12:00 - 14:00 Jack Erskine 131 Lab 1
22 Jul - 25 Aug
9 Sep - 20 Oct
03 Thursday 11:00 - 13:00 Jack Erskine 131 Lab 1
22 Jul - 25 Aug
9 Sep - 20 Oct
04 Monday 13:00 - 15:00 Jack Erskine 136 Lab 4
22 Jul - 25 Aug
9 Sep - 20 Oct
05 Friday 14:00 - 16:00 Jack Erskine 131 Lab 1
22 Jul - 25 Aug
9 Sep - 20 Oct

Examinations, Quizzes and Formal Tests

Test A
Activity Day Time Location Weeks
01 Friday 19:00 - 21:00 Jack Erskine 131 Lab 1
16 Sep - 22 Sep
02 Friday 19:00 - 21:00 Jack Erskine 133 Lab 2
16 Sep - 22 Sep
03 Friday 19:00 - 21:00 Jack Erskine 134 Lab 3
16 Sep - 22 Sep
04 Friday 19:00 - 21:00 Jack Erskine 136 Lab 4
16 Sep - 22 Sep
05 Friday 19:00 - 21:00 Jack Erskine 001 Computer Lab
16 Sep - 22 Sep
06 Friday 19:00 - 21:00 Jack Erskine 010 Computer Lab
16 Sep - 22 Sep
07 Friday 19:00 - 21:00 Jack Erskine 248 Computer Lab
16 Sep - 22 Sep

Timetable Note

Depending on final student numbers, some of the advertised lab/tutorial streams may not run. Final lab/tutorial options will be available for self-allocation closer to the start of the semester through My Timetable.

Course Coordinator

Kourosh Neshatian

Assessment

Assessment Due Date Percentage  Description
Assignments 5% 2 super quizzes
Quizzes 16.5% Weekly quizzes
Lab Test 20%
Final Exam 58.5%


Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.

Textbooks / Resources

Recommended Reading

Poole, David L.1958- , Mackworth, Alan K; Artificial intelligence : foundations of computational agents ; Cambridge University Press, 2010.

Russell, Stuart J. , Norvig, Peter; Artificial intelligence : a modern approach ; 3rd ed; Prentice Hall, 2010.

Notes

There are several important documents available online about departmental regulations, policies and guidelines at the following site. We expect all students to be familiar with these.

Notices about this class will be posted to the class forum in the Learn system.

COSC students will also be made members of a class called “CSSE Notices”, where general notices will be posted that apply to all classes (such as information about building access or job opportunities).

Additional Course Outline Information

Grade moderation

The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:
1. You must achieve an average grade of at least 50% over all assessment items.
2. You must achieve an average mark of at least 45% on invigilated assessment items.

If you satisfy both these criteria, your grade will be determined by the following University-wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a C+ grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.

Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control.

Applications for special consideration should be submitted via the Examinations Office website within five days of the assessment.

Where an extension may be granted for an assessment, this will be decided by direct application to the Department and an application to the Examinations Office may not be required.

Special consideration is not available for items worth less than 10% of the course.

Students prevented by extenuating circumstances from completing the course after the final date for withdrawing, may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the end of the main examination period for the semester.

Indicative Fees

Domestic fee $942.00

International fee $4,988.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

Minimum enrolments

This course will not be offered if fewer than 10 people apply to enrol.

For further information see Computer Science and Software Engineering .

All COSC367 Occurrences

  • COSC367-24S2 (C) Semester Two 2024