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Curricular information is subject to change
By the end of this module a student should be able to:
(1) Explain the underlying principles, and evaluate the advantages and limitations, of the AI approach to problem solving.
(2) Describe and implement a range of search algorithms and discuss the limitations associated with each.
(3) Demonstrate an understanding of the application of artificial intelligence techniques to game playing.
(4) Define the concepts of different planning systems and explain how they differ from classical search techniques.
(5) Have a basic understanding of different machine learning approaches, neural networks and genetic algorithms.
(6) Understand the open challenges in the field of Human-Centered AI, can distinguish one recommendation approach from another and understand some of the current open challenges in this research area.
(7) Demonstrate that they have researched the module content beyond lectures.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Practical | 24 |
Autonomous Student Learning | 60 |
Total | 108 |
Students should have a solid knowledge of Data Structures and Algorithms and reasonable programming skills (pref Java).
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Class Test: Online Exam | Unspecified | n/a | Graded | No | 10 |
Class Test: Online Exam | Week 12 | n/a | Graded | No | 25 |
Continuous Assessment: Assignment Sheets & Implementation Tasks | Varies over the Trimester | n/a | Graded | No | 50 |
Assignment: Flipped Classroom Assessment | Unspecified | n/a | Graded | No | 15 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
• Online automated feedback
Not yet recorded.