Information Retrieval & Data Mining

Course aim

To master both the theoretical and practical aspects of information retrieval and data mining.

Course content

Basic theories and mathematical models of information retrieval and data mining are covered, with 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 are also covered.

Overview of the fields
Some basic concepts of information retrieval and data mining, such as the concept of relevance, association rules, and knowledge discovery. Understand the conceptual models of an information retrieval and knowledge discovery system.

Indexing
Various indexing techniques for textual information items, such as inverted indices, tokenization, stemming and stop words.

Retrieval Methods
Popular retrieval models: 1 Boolean, 2. Vector space, 3 Binary independence, 4 Language modelling. Probability ranking principle. Other commonly-used techniques include relevance feedback, pseudo relevance feedback, and query expansion.
Evaluation of Retrieval Performance

Measurements
Average precision, NDCG, etc. "Cranfield paradigm" and TREC conferences.
Personalisation and Usage Mining
Basic techniques for collaborative filtering and recommender systems, such as the memory-based approaches, probabilistic latent semantic analysis (PLSA), personalized web search through click-through data.

Data Mining
Basic techniques, algorithms, and systems of data mining and analytics, including frequent pattern and correlation and association analysis, anomaly detection, and click-through modelling.

Emerging Areas
MapReduce and Sparck; Learning to Rank; Portfolio retrieval and Risk Management; Deep Learning

Textbook(s)

Nil

Prerequisite(s)

Basic understanding of probability and statistics and proficient in java programming, as demonstrated by a least one programming project in the past.

Corequisite(s)

Nil

Teaching method

Component	Duration
EXTERNAL, MAWSON LAKES, ONLINE
External (Delivered by UCL London)	N/A x N/A

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Assignment, Examination

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
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* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.