Information Package
baskent.edu.tr

Program of Artificial Intelligence Engineering (English)

AI307

DATA MINING AND ENGINEERING

Course Code
AI307
Semester
Fifth Term (Fall)
Theory+Practice
3 + 1
ECTS
5
Course TypeCompulsory Course
Course LevelBachelor's Degree
Year of Study3
SemesterFifth Term (Fall)
ECTS5
Name of Lecturer(s) -
Learning Outcomes

At the end of this course, the students;

  1. Will be able to apply some linear algebra and statistics techniques using MATLAB.
  2. Will have a better understanding of data, information and knowledge.
  3. Will learn data mining (DM), and data exploration.
  4. Will learn Classification, Association Analysis, Cluster Analysis, and Anomaly Detection techniques and apply some algorithms on the given data.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme Components
Course DescriptionIntroduction to Data Mining DM, Some Linear Algebra and Statistics Techniques and MATLAB applications. Data-Information-Knowledge : Relations and Differences. Data Mining (DM) and Data Exploration. Classification - General. Classification - Algorithms. Classification - Applications. Association Analysis - General. Association Analysis - Algorithms and Applications. Cluster Analysis - General. Cluster Analysis - Algorithms. Cluster Analysis - Applications. Anomaly Detection - General. Anomaly Detection - Application.
Course Contents
WEEK TOPICS
1st Week Introduction to Data Mining.
2nd Week DM, Some Linear Algebra and Statistics Techniques and MATLAB applications.
3rd Week Data-Information-Knowledge : Relations and Differences.
4th Week Data Mining (DM) and Data Exploration.
5th Week Classification - General & Algorithms.
6th Week Classification - Applications.
7th Week Association Analysis - General.
8th Week Mid-term
9th Week Association Analysis - Algorithms and Applications.
10th Week Cluster Analysis - General.
11th Week Cluster Analysis - Algorithms.
12th Week Cluster Analysis - Applications.
13th Week Anomaly Detection - General.
14th Week Anomaly Detection - Algorithms and Applications.
Recommended or Required Reading* Course Book: Tan, P.N., M. Steinbach, V. Kumar, "Data Mining", Pearson, 2007.
* Additional Resources:
* Turban, E., Sharda, R. & Delen, D.,"Decision Support and Business Intelligence Systems", Pearson, 9th Ed., 2011.
* Olson, D. &Shi, Y.,"Introduction to Business Data Mining", McGraw-Hill, 2007.
* Han, J. and M. Kamber, "Data Mining", Morgan Kaufman, 2.ed. 2006.
* Mitchell, T.M.,"Machine Learning", McGraw-Hill, 1997.
Planned Learning Activities and Teaching Methods
  • Presentation
  • Project
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 35
Project 1 25
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
Preparation for Quiz
Individual or group work
Preparation for Final exam
Course hours
Preparation for Midterm exam
Laboratory (including preparation)
Final exam
Homework
Total Work Load0
Total Work Load / 30 (h)0
ECTS Credit of the Course5
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12