CSCI3230 Fundamentals of Artificial Intelligence


Course code CSCI3220
Course title Fundamentals of Artificial Intelligence
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.
(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);
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);
5. succeed in research or industry related to computer science (K/S/V);
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);
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured