| Course code | CSCI3220 |
| Course title | Fundamentals of Artificial Intelligence 人工智能之基本原理 |
| Course description | This course introduces the basic concepts and techniques of artificial intelligence. Knowledge representation: predicate logic and inference, semantic networks, scripts and frames, and object-oriented representation. Searching: such as A*, hill-climbing, minimax and alpha-beta pruning. Planning: the frame problem and the STRIPS formalism, representation schemes and planning strategies. Neural networks: learning algorithms, neural architecture and applications. Natural language processing. Knowledge acquisition and expert systems: properties, techniques and tools of expert systems 本科介紹人工智能之基本概念及技術。知識表示法：謂詞邏輯及推論、語義網絡、目標面向的表示法。檢索：例如A* 、攀山、極大極小及α – β 刪節。計劃：結構問題及STRIPS形式方法、表示方案及計劃策略。神經網絡：學習算法、神經體系結構及應用、自然語言處理。知識收集及專家系統：特性、技術及專家系統工具。 |
| Unit(s) | 3 |
| Course level | Undergraduate |
| Pre-requisite | CSCI2100 or 2520 or ESTR2102 |
| Exclusion | ESTR3108 |
| Semester | 1 or 2 |
| Grading basis | Graded |
| Grade Descriptors | A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. |
| Learning outcomes | Students will be able to: 1. Use agents to model AI problems; 2. Use search techniques such as A* to search for optimal solutions for AI problems and to play games; 3. Use various logic to represent knowledge and to do reasoning and build expert systems; 4. Use computer learning techniques to acquire real life knowledge in an appropriate representation model (e.g. decision tree and neural networks); 5. Derive learning rules from first principle; 6. Solve real life problems (e.g.classifications and prediction) by such models; 7. Estimate complexity of AI algorithms and prove theorems by contradiction and other techniques; 8. Use computer vision techniques such edge detection to extract features. |
| Assessment (for reference only) |
Exam: 55% Project: 30% Assignments: 15% |
| Recommended Reading List | 1. “Artificial Intelligence- A Modern Approach” Stuart Russell and Peter Norvig, Prentice Hall, 2003(2nd edition). (main) 2. “Artificial Intelligence” George F. Luger,(5th edition), AddisonWesley, 2005 |
| CSCIN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. identify, formulate, and solve computer science problems (K/S); | TP |
| 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); |
P |
| 3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); | TP |
| 4. communicate effectively (S/V); |
P |
| 5. succeed in research or industry related to computer science (K/S/V); |
TP |
| 6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); | TP |
| 7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V); | P |
| 8. practise high standard of professional ethics (V); | |
| 9. draw on and integrate knowledge from many related areas (K/S/V); |
P |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured | |