Information Package / Course Catalogue - Adnan Menderes University

General Information Unit Staff Academic Information Programme Outcomes Course Structure Diagram Contact Information

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Decision Trees

Course Code	: BİS628
Course Type	: Area Elective
Couse Group	: Third Cycle (Doctorate Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 4
Prt.	: 0
Credit	: 4
Lab	: 0
ECTS	: 4

Objectives of the Course

This course covers basically how the decisions given and what methods are used. In this course, mathematical, statistical, and psychological approaches are studied and taught.

Course Content

Decision theory, types and characteristics of decision making; components, elements, structure, process, models and matrix of decision making. Decision making problem and general characteristics. Decision situations. Multi-criteria decision making Use of statistics and probability in design decision making. Squential decision making.

Name of Lecturer(s)

Learning Outcomes

1.	Analyse the problems by analytical thinking
2.	Configure and analyze problems
3.	Solve data analytic problems using decision making
4.	Analyze problems and provides solutions to utility and game theory perspective
5.	Apply the multi criteria decision making techniques to complex systems
6.	Make group decisions and apply the decisions to the solution of the problems

Recommended or Required Reading

Weekly Detailed Course Contents

Week 1 - Theoretical

Structure of decision trees and basic concepts

Week 2 - Theoretical

Spliting rules in forming a decision tree

Week 3 - Theoretical

Examination of stopping criterias

Week 4 - Theoretical

Examination of overfitting and inadequate fitting problems

Week 5 - Theoretical

Pruning techniques

Week 6 - Theoretical

Regression trees

Week 7 - Theoretical

Classification trees

Week 8 - Theoretical

Literature review and discussion (Midterm exam)

Week 9 - Theoretical

CART algorithm and properties

Week 10 - Theoretical

Regression and classification with CART algorithm in R

Week 11 - Theoretical

CHAID algorithm and properties

Week 12 - Theoretical

Regression and classification with CHAID algorithm in R

Week 13 - Theoretical

Random forests method and properties

Week 14 - Theoretical

Regression and classification by random forests method in R

Week 15 - Final Exam

Final exam

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Attending Lectures	1	%5
Assignment	1	%5
Midterm Examination	1	%20
Final Examination	1	%70

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	0	4	56
Midterm Examination	1	15	2	17
Final Examination	1	20	2	22
TOTAL WORKLOAD (hours)				95

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7
OÇ-1	4	4	4	4	4	4	4
OÇ-2	4	4	4	4	3	2	2
OÇ-3	4	4	4	4	3	3	4
OÇ-4	4	4	4	4	3	4	4
OÇ-5	4	4	4	4	3	3	4
OÇ-6	4	4	4	4	3	2	2

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