This course is an introduction to Artificial intelligence.
Module Aim
The aim of this module is to provide the learner with a solid foundation of the
key concepts in artificial intelligence and knowledge-based systems.
Intended Learning Outcomes
On completion of this module the student should be able to:
- Have knowledge and understanding of key concepts of artificial
intelligence such as intelligent agents, knowledge representation,
reasoning under uncertainty and methods for machine learning. - Analyse AI problems to determine appropriate methods of design,
testing and evaluation.
Formulate a research project involving AI concepts. - Find and use AI tools to solve problems.
Indicative Content
a) Introduction to AI
i. history of AI,
ii. recent developments in AI,
iii. types of AI
b) Intelligent Agents
i. Agent Architecture and Hierarchical control,
ii. Uninformed and Informed Search,
iii. Local Search and Optimisation problems Game strategies
c) Introduction to Machine Learning
i. Learning problems,
ii. Decision Tree Learning,
iii. Instance-Based Learning,
iv. Bayesian Learning,
v. Artificial Neural Networks,
vi. Deep Learning and Reinforcement Learning,
vii. Support Vector Machines
d) Knowledge representation and planning
i. Propositional and First Order Logic,
ii. Natural Language Processing,
iii. Expert System
e) AI for Business Planning and Decision Making
i. Applications of AI Technologies: e.g., in Computer Vision, Machine
Translation, Education, Supply Chain, Medicine, Retail, Security,
etc.,
ii. Data and Data Sources
iii. Legal and ethical Issues in Artificial Intelligence.
- Teacher: KAI Admin