University of Technology Sydney

41040 Introduction to Artificial Intelligence

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 6 cp

Subject level:

Undergraduate

Result type: Grade and marks

Requisite(s): 48023 Programming Fundamentals OR 41039 Programming 1

Recommended studies:

33230 Mathematics 2; 37181 Discrete Mathematics; linear algebra, probability, statistics, discrete logic

Description

This subject helps students develop good understanding of concepts and fundamental algorithm ideas of Artificial Intelligence (AI) in some areas: learning, natural language processing, computer vision, searching, knowledge representation, inference and reasoning. Through a series of lectures and hands-on laboratory experiments designated for specific AI techniques in the targeted areas, students develop good understanding of these techniques, ability to make good judgement on model/algorithm selection for given tasks, and competence in applying these AI techniques in simple applications.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Exemplify applications of AI techniques. (D.1)
2. Explain key ideas of common AI techniques. (D.1)
3. Apply common AI techniques to solve simple real world problems based on existing implementations. (C.1)
4. Communicate effectively in both oral and written forms. (E.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
  • Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
  • Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)

Teaching and learning strategies

This subject will be delivered through weekly lectures, hands-on laboratories/tutorials with aid of an online learning management system. During the lectures, the selected Al techniques will be explained and discussed. Students are required to read and/or view designated materials for each lecture.

During a laboratory session, students will practice the AI techniques covered in the week in small groups to solve simple problems with help from a lab facilitator. They will learn from the lab demo programs and develop competence of applying the AI techniques to solve simple problems in an assignment.

During a tutorial session, students will do some exercises to show their understanding of common knowledge representation and inference/reasoning techniques supported by a tutor.

Understanding of the AI techniques covered in lectures and their applications will be assessed through the final exam.
Students will develop a good understanding of AI techniques through a series of AI technique demons. They will develop their competence of applying some selected AI techniques through a group AI project. Their communication skills will be developed through communication between group members, written reports of AI projects and project presentations.

Content (topics)

  1. Introduction of concepts and evolution of AI
  2. Machine learning
  3. Natural language processing
  4. Computer vision
  5. Searching
  6. Knowledge representation
  7. Inference and Reasoning

Assessment

Assessment task 1: AI technique demos

Intent:

Assess students’ ability to articulate their understanding of applying artificial intelligence techniques to solve practical problems based on existing libraries.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1, D.1 and E.1

Type: Presentation
Groupwork: Individual
Weight: 40%
Length:

Each short video is no longer than 5 minutes (penalty will apply for exceeding this limit)

Assessment task 2: AI Project

Intent:

Assess a student’s ability to work in a small group, collaboratively develop a showcase of applying some selected AI techniques to solve an interesting real-world problem and effectively present the outcomes of the project.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and E.1

Type: Project
Groupwork: Group, group and individually assessed
Weight: 30%
Length:

No line limit to the solution program. The video presentation is no longer than 15 minutes (penalty will apply for exceeding this limit).

Assessment task 3: Final Exam

Intent:

Provide an opportunity for students to challenge their understanding of concepts and key ideas of artificial intelligence techniques.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1 and 2

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

D.1

Type: Examination
Groupwork: Individual
Weight: 30%
Length:

120 minutes

Minimum requirements

To pass this subject students must achieve an overall mark of 50% or greater.

Required texts

Stuart J. Russell and Peter Norvig, Artificial Intelligence -- A Modern Approach, 4th Edition, Pearson, 2021.

Textbook website: aima.cs.berkeley.edu

References

Details see the subject reading lists