COMPGI15 - Information Retrieval & Data Mining

This database contains the 2016-17 versions of syllabuses. Syllabuses from the 2015-16 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

CodeCOMPGI15 (Also taught as: COMPM052)
YearMSc
PrerequisitesNormally offered only to students in computer science related programmes because basic programming skills are required. Basic understanding of probability and statistics and proficient in java programming, as demonstrated by a least one programing project in the past.
Term2
Taught ByJun Wang (50%), Emine Yilmaz (50%)
AimsThe course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. Practical retrieval and data mining applications such as web search engines, personalisation and recommender systems, business intelligence, and fraud detection will also be covered.
Learning OutcomesStudents are expected to master both the theoretical and practical aspects of information retrieval and data mining. At the end of the course student are expected to understand 1. The common algorithms and techniques for information retrieval (document indexing and retrieval, query processing, etc). 2. The quantitative evaluation methods for the IR systems and data mining techniques. 3. The popular probabilistic retrieval methods and ranking principles. 4. The techniques and algorithms existing in practical retrieval and data mining systems such as those in web search engines and recommender systems, including the recently popular topic of deep learning. 5. The challenges and existing techniques for the emerging topics of MapReduce, portfolio retrieval and deep learning.