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Module details
CO528 Introduction to Intelligent Systems (15 credits)
Syllabus
- Introduction:
- The motivation to create intelligent machines and an initial discussion about the nature of intelligence.
- Philosophy of AI:
- Main philosophical components of AI, investigating topics such as the nature of intelligence, learning and consciousness.
- Social implications of intelligent machines.
- A Brief history of AI.
- Similarities and differences between AI and Computational Intelligence.
- The evolution of the mind.
- State-space search algorithms:
- The concepts of state space and heuristic evaluation function.
- Depth-first, breadth-first and best-first search. A* Algorithm.
- Knowledge representation:
- Main concepts of different kinds of knowledge representation, such as logic (both classic and fuzzy logic), case-based representations, and sub-symbolic/connectionist representations.
- Principles of AI algorithms using each of these kinds of representations.
- Introduction to Machine Learning:
- Main concepts of supervised learning, unsupervised learning and reinforcement learning.
- Basic ideas of algorithms for each of these kinds of machine learning problems.
- Introduction to Biologically-Inspired Computation:
- The motivation for biologically-inspired computation.
- Overview of biological systems that serve as inspiration for AI, such as the brain, evolutionary theory, swarming insects and immune systems. Introduction to those systems' artificial counterparts in the context of AI, i.e. artificial neural networks, evolutionary algorithms, swarm intelligence algorithms and artificial immune systems.
- Applications and case studies:
- A number of case studies which will illustrate the application of ideas, techniques and technologies from the remainder of the course.
Note
This web page provides advance information about a module due to run in the coming academic year. We believe the details are accurate at the time of writing but they may be subject to change.