| Course code | ENGG5106 |
| Course title | Information Retrieval and Search Engines 訊息檢索與搜索引擎 |
| Course description | This course surveys the current research in information retrieval for the Internet and related topics. This course will focus on the theoretical development of information retrieval systems for multimedia contents as well as practical design and implementation issues associated with Internet search engines. Topics include probabilistic retrieval, relevance feedback, indexing of multimedia data, and applications in e-commerce. 本課程涵蓋互連網訊息檢索研究及其相關課題。課程將集中討論多媒體訊息查詢系統的理論研究，同時也將涉及互連網搜索引擎的實際應用設計和實現。課題包括：概率查詢、相關信息反饋、多媒體數據索引以及電子商務方面的應用。 |
| Unit(s) | 3 |
| Course level | Postgraduate |
| Exclusion | CSCI5250 |
| 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 | At the end of the course of studies, students will have acquired the ability to 1. understand the infrastructure and techniques behind Search Engines; 2. know the existing literature and research challenges in the area of Information Retrieval; 3. realize how to organize and manage huge amount of information, such as that from on the Web; 4. practice a real project in information retrieval system and/or search engine prototype. |
| Assessment (for reference only) |
Lab reports: 40% Essay test or exam: 40% Others: 30% |
| Recommended Reading List | Required: 1. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.1. Managing Gigabytes, by I. Witten, A. Moffat, and T. Bell. 2. Information Retrieval: Algorithms and Heuristics by D. Grossman and O. Frieder. 3. Modern Information Retrieval, by R. Baeza-Yates and B. Ribeiro-Neto. 4. Search Engines: Information Retrieval in Practice, by Bruce Croft, Donald Metzler and Trevor Strohman. 5. Information Retrieval: Implementing and Evaluating Search Engines, by Stefan Buettcher, Charles L. A. Clarke and Gordon V. Cormack. 6. Other papers associated with each topic |
| 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); | |
| 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); |
TP |
| 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); |
P |
| 5. succeed in research or industry related to computer science (K/S/V); |
T |
| 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); | |
| 8. practise high standard of professional ethics (V); | |
| 9. draw on and integrate knowledge from many related areas (K/S/V); |
T |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured | |