Information Package
baskent.edu.tr

Master's of Computer Engineering without Thesis

BİL562

INFORMATION FILTERING

Course Code
BİL562
Semester
-
Theory+Practice
3 + 0
ECTS
10
Course TypeElective Course
Course LevelMaster's Degree Without Thesis
Year of Study-
Semester-
ECTS10
Name of Lecturer(s) -
Learning Outcomes

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 DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme ComponentsNone
Course DescriptionConcepts 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
Recommended or Required Reading1. 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).
Planned Learning Activities and Teaching Methods
  • Lecture
  • Questions/Answers
  • Presentation
  • Practice
  • Project
  • Report Preparation
  • Problem Solving
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Assignment 1 10
Project 1 20
Total(%) 60
Contribution of In-term Studies to Overall Grade(%) 60
Contribution of Final Examination to Overall Grade(%) 40
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam 1 2 2
Preparation for Quiz
Individual or group work 14 11 154
Preparation for Final exam 1 69 69
Course hours 14 3 42
Preparation for Midterm exam 1 44 44
Laboratory (including preparation)
Final exam 1 2 2
Homework
Total Work Load313
Total Work Load / 30 (h)10.43
ECTS Credit of the Course10
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4 LO5 LO6
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K2 X X
K3 X X
K4 X X
K5 X X X X
K6 X X
K7 X X
K8 X X X X
K9
K10 X X
K11 X X
K12 X X X X X X