ITC397 Introduction to Artificial Intelligence (8)

Artificial intelligence is driving the next generation of applied software solutions embedded in the infrastructure of various products for medical diagnoses, robotics, search engines, and self-driving cars. This subject provides students with a solid foundation in the design and development of Artificial Intelligence (AI) systems. The students will learn how machines can engage in learning, reasoning and planning with a special focus on Machine Learning (ML). The subject will discuss recent developments in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The subject will also empower students with development skills in AI system design using the Python programming language. This will help students solve everyday problems using AI techniques.

No offerings have been identified for this subject in 2021.

Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System



One session


School of Computing and Mathematics

Enrolment Restrictions

Available to undergraduate students only.



Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to understand the fundamental theories, evolution, and applications of AI;
  • be able to evaluate various AI search algorithms, such as uninformed, informed, heuristic, and constraint satisfaction;
  • be able to understand the basic requirements for designing neural networks and machine learning models;
  • be able to apply machine learning solutions to real-world problems; and
  • be able to evaluate and use existing models for solving natural language processing and/or computer vision problems.


This subject will cover the following topics:
  • Introduction to Artificial Intelligence, History and Applications
  • Agent Search Strategies and Reasoning
  • Python for AI Applications
  • Biological Nets and Perceptron
  • The Learning Rules and Gradient Descent
  • Multilayer Perceptron and Backpropagation
  • Deep Learning: the Basics
  • Recognition with Computer Vision
  • Understanding Natural Language Processing
  • Robotics and Reinforcement Learning

The information contained in the CSU Handbook was accurate at the date of publication: May 2021. The University reserves the right to vary the information at any time without notice.