IE502014 Artificial Intelligence - Overview
The following plan is tentative, based on last year’s plan. There will be changes, but the rough outline will remain.
Session | Topic | Reading | Keywords |
---|---|---|---|
1 | Introduction 12 January 2023 | R&N Ch. 1 | What is intelligence? Is AI possible? |
2 | Rational Agents | R&N Ch. 2 | Instrumental reason, practical reason, etc. |
3 | Search | R&N Ch. 3.1-3.4 | Search and Problem Solving |
4 | Heuristic Search | R&N Ch. 3.5-3.7 | Heuristic Search |
5 | Complex Environments | R&N Ch. 4 | Local Search, Partional Information etc. |
6 | Adversarial Search | R&N Ch. 6 | Game Theory |
- | Midterm | 20-24 February - own work | |
7 | Optimisation and GA | H&H Chapter 1-2 | Genetic Algoritms |
8 | Continuous GA | H&H Chapter 3-4 | Genetic Algoritms |
9 | Game Theory | Game Theory, Simulation, Genetic Algorithm | |
10 | Machine Learning and Statistics | R&N Chapter 19 | |
11 | Markov Decision Processes MDP | R&N Chapter 16 | |
- | Easter | 3-10 April - holiday | |
12 | Reinforcement Learning | R&N Chapter 23 | |
13 | Deep Q-Learning for Reinforcement Learning | R&N Chapter 16/23 | |
14 | AI Ethics 24 April 2023 | Implementation of ethical constraints |
Considering
- Limits of AI - a review of ChatGPT and github copilot
- R&N Chapter 7: Logical Agents
- R&N Chapter 12: Acting under Uncertainty
Key topics
- Ethics.
- What is intelligence?
- what is artificial intelligence?
- is artificial intelligence at all possible?
- Concerns about AI
- Machine Morality
- What is intelligence?
- Intelligence Agents
- State Machines and Graph Algorithms
- Markov Decision Processes and Reinforcement Learning
- Optimisation and Evolutionary Algorithms
Note There are other modules on machine learning, so this remains out of scope.