At the end of this course, the students; 1) Describe the basic concepts and processes of information retrieval systems and data mining techniques. 2) Describe the common algorithms and techniques for information filtering. 3) Describe the quantitative evaluation methods for the IR systems and data mining techniques. 4) Describe the popular probabilistic filtering methods and ranking principle. 5) Know the techniques and algorithms existing in practical filtering and data mining systems such as those in web search engines and the Amazon book/ Last.FM recommender systems. 6) Describe the challenges and existing techniques for the emerging topics of MapReduce, portfolio filtering and online advertising.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Concepts of information filtering and data mining. Indexing techniques for textual information items. Filtering Methods. Evaluation of Filtering Performance. Techniques for collaborative filtering and recommender systems. Techniques, algorithms, and systems of data mining and analytics. Peer-to-peer information filtering and MapReduce
COURSE CONTENTS
WEEK
TOPICS
1st Week
Concepts of information filtering and data mining;
2nd Week
Concepts of information filtering and data mining;
3rd Week
Indexing techniques for textual information items;
4th Week
Indexing techniques for textual information items;
5th Week
Filtering Methods;
6th Week
Filtering Methods;
7th Week
Evaluation of Filtering Performance;
8th Week
Mid-term
9th Week
Techniques for collaborative filtering and recommender systems;
10th Week
Techniques for collaborative filtering and recommender systems;
11th Week
Techniques, algorithms, and systems of data mining and analytics;
12th Week
Techniques, algorithms, and systems of data mining and analytics;
13th Week
Peer-to-peer information filtering and MapReduce
14th Week
Peer-to-peer information filtering and MapReduce
RECOMENDED OR REQUIRED READING
1. Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008. 2. Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Addison-Wesley, 2006 3. Gigabytes (2nd Ed.) Ian H. Witten, Alistair Moffat and Timothy C. Bell. (1999), Morgan Kaufmann, San Francisco, California. 4. Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer (2006).
