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CS5486 Intelligent Systems
Part I Course Duration: One semester
Credit Units: 3
Level: P5
Medium of Instruction: English
Prerequisites: Nil
Precursors:
CS2302 Data Structures and Algorithms or
CS2468 Data Structures and Data Management or
CS3334 Data Structures or
Any equivalent course at undergraduate level
Equivalent Courses: Nil
Exclusive Courses: Nil Part II Course Aims
This course aims to equip students with the skills of problem solving using artificial intelligence (AI) techniques through a demonstrable knowledge in a range problem solving methods and the associated knowledge representation and machine learning techniques. Course Intended Learning Outcomes (CILOs)
Upon successful completion of this course, students should be able to: No. | CILOs | Weighting
(if applicable) | 1. | demonstrate knowledge of the fundamental principles of intelligent systems; | | 2. | distinguish between conventional computer applications and intelligent applications; | | 3. | analyze and compare the relative merits of a variety of AI problem solving techniques; | | 4. | formulate and analyse intelligent system problems; | | 5. | design and implement intelligent problem solving methods. | |
Teaching and Learning Activities (TLAs)
(Indicative of likely activities and tasks designed to facilitate students’ achievement of the CILOs. Final details will be provided to students in their first week of attendance in this course)
Teaching pattern:
Suggested lecture/tutorial/laboratory mix: 2 hrs. lecture; 1 hr. tutorial. CILO No. | TLAs | Hours/week
(if applicable) | CILO 1 -
CILO 5 | The course will consist of a balance-mixed of theory and practice. Through a combination of formal lectures and coursework, the students will become able to apply major AI concepts and problem solving approaches to solving real life problems. The coursework will consist of a special topic study in which the student performs a critical analysis of a recently proposed AI approach, the findings of which are to be described in a presentation, and a project that involves applying suitable AI algorithms to solve a real world problem. | |
Assessment Tasks/Activities
(Indicative of likely activities and tasks designed to assess how well the students achieve the CILOs. Final details will be provided to students in their first week of attendance in this course)
Examination duration: 2 hours
Percentage of coursework, examination, etc.: 30% CW; 70% Exam
The course ILOs are assessed using the following approaches: CILO No. | Type of Assessment Tasks/Activities | Weighting
(if applicable) | Remarks | CILO 1,
CILO 2 | ILO 1 and 2 will be assessed mainly through examination that aims to assess the depth of understanding on a range of AI concepts and techniques. | | | CILO 4,
CILO 5 | ILO 4 and 5 will be assessed through coursework for which the students have to demonstrate practical as well as analytical skills to select, implement and evaluate the effectiveness and efficiency of the chosen AI techniques and approaches to solving a specific AI problem. | | | CILO 3 | ILO 3 will be assessed through a combination of examination and coursework. | | |
Grading of Student Achievement: Refer to Grading of Courses in the Academic Regulations
Grading pattern: Standard (A+, A, A-…F)
For a student to pass the course, at least 30% of the maximum mark for the examination must be obtained. Part III Keyword Syllabus: Artificial intelligence. State space search. Heuristic search, Knowledge representations. Rule-based systems, Stocastic search algorithms, Logical inference, Uncertainy reasoning methods, Machine Learning, Decision trees. Syllabus 1. | Overview
An introduction to the goals and objectives of AI as a discipline and its relationship with other disciplines. Approaches in AI. Major components in a typical intelligent system.
| | 2. | Search Methods
Basic concepts of graph and tree search. Three simple search methods: breadth-first search, depth-first search, iterative deepening search. Heuristic search methods: best-first search, admissible evaluation functions, hill-climbing search. Optimisation and search such as stochastic annealing and genetic algorithm.
| | 3. | Knowledge representation and logical inference
Issues in knowledge representation. Structured representation, such as frames, and scripts, semantic networks and conceptual graphs. Formal logic and logical inference. Knowledge-based systems structures, its basic components. Ideas of Blackboard architectures.
| | 4. | Reasoning under uncertainty and Learning
Techniques on uncertainty reasoning such as Bayesian reasoning, Certainty factors and Dempster-Shafer Theory of Evidential reasoning. A study of different learning and evolutionary algorithms, such as statistical learning and induction learning. |
Related Links
Department of Computer Science
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