COM1006 Artificial Intelligence and its applications – Common Core Curriculum Office (CCCO)

Department

Computing

Semester

AY2018/19 Sem 2, AY2019/20 Sem 2, AY2020/21 Sem 2, AY2021/22 Sem 1, AY2021/22 Sem 2, AY2022/23 Sem 1, AY2022/23 Sem 2, AY2023/24 Sem 1, AY2023/24 Sem 2

Method

Lecture 1.5 hours + Tutorial 1.5 hours

Cluster

3 (Science and Technology)

Prerequisite

GEN1000 Perspectives on General Education

Exclusion

Nil

Module Description

This module aims to provide a broad overview of both classical and contemporary Artificial Intelligence (AI) technologies and their many applications. It begins with the very concept of AI: what do we mean by intelligent computer applications, and the historical development of AI systems. Next, from knowledge representation to problem solving, from neural networks to deep learning, the module then introduces the common AI technologies from the classical approaches to the recent developments in computational intelligence. The scientific methodologies involved in the development of AI systems are discussed. Recent developments in AI applications and their social implications are investigated. This module is designed for students without computing or programming background.

Module Intended Learning Outcomes (MILO)

Upon completion of this module, students should be able to:
a. Understand the historical and contemporary development of AI technologies and their applications;
b. Examine the common AI techniques and their basic principles;
c. Discuss the scientific mindset and scientific methodologies involved in the development of AI applications.
d. Analyze the use of AI technology in everyday applications and their social implications

Module Content

1. Artificial Intelligence (AI)

1.1 What is Artificial Intelligence?
1.2 History of Artificial Intelligence

2. Classical Artificial Intelligence

2.1 Knowledge representation and reasoning
2.2 Searching algorithms
2.3 Rule based systems and expert systems
2.4 Intelligent agents

3. Computational Intelligence and Machine Learning

3.1 Introduction to computational intelligence and
machine learning
3.2 Artificial neural networks and deep learning
3.3 Tools for machine learning (e.g., Weka)

4. The Scientific Method of AI application Development

4.1 Problem identification and problem definition
4.2 Modelling
4.3 Prototyping and experimentation
4.4 Testing and evaluation

5. Impacts of AI applications

5.1 Impacts of AI technologies to human behaviour
5.2 Impacts of AI technologies to the society

Assessment Methods

1. Participation (10 %)
2. Exercises/Assignments (30 %)
3. Quizzes/Tests (30 %)
4. Project
a. Presentation (10 %)
b. Essay (20 %)

Texts & References

Reference materials:
1. Rouhiainen, L., & Estra, C. (2018). Artificial intelligence: 101 things you must know today about our future. Charleston: CreateSpace.
2. Finlay, S. M. (2017). Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. Relativistic.
3. Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.
4. Frank, M., Roehrig, P., & Pring, B. (2017). What To Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data. John Wiley & Sons.
5. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
6. Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Data mining: practical machine learning tools and techniques. Morgan Kaufmann.