|Course title||Introduction to Discrete Mathematics and Algorithms
|Course description||This course introduces logic, combinatorics, recurrence relations and graph theory. Design and analysis of algorithms: greedy method, divide and conquer, and dynamic programming. Fundamental algorithms including sorting, graph algorithms, number-theoretic algorithms and numerical algorithms. Introduction to NP-completeness.
本科介紹邏輯，組合學，遞歸關係與圖論。算法之設計與分析：優先策略，分治策略與動態規劃。基本算法包括排序，圖算法，數論算法與數值算法。NP – 完全性。
|Pre-requisite||CSCI2100 or 2520 or ESTR2102|
|Exclusion||CSCI3160 or ENGG2440 or ESTR2004 or 3104|
|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. understand the concepts of logic, sets, functions, and graphs;
2. analyze the time complexity of an algorithm;
3. obtain good knowledge of fundamental sorting, graph, number-theoretic, and numerical algorithms;
4. design greedy, divide-and-conquer, and dynamic-programming algorithms to solve new problems;
5. comprehend the concepts of NP-hardness and basic reductions.
(for reference only)
|Final exam: 50%
Mid-term exam: 25%
|Recommended Reading List||1. Discrete Mathematics and Its Applications (5th Ed.), by K.H. Rosen, McGraw Hill.
|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);||T|
|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);||T|
|7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);|
|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|