Ensemble Learning

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Statistics and Probability, Data Mining, Introduction to Machine Learning

Course Contents

Reasons of ensemble learning, advantages over individual learners, Bagging, Random subspaces, Random forests, Rotation forests, Error correcting code-based methods, Factors affecting the success of ensemble learning, Classification, clustering, ensemble learning applications in the areas of regression, ensemble learning methods, Meta learning

Weekly Schedule

1) Purpose of collective learning, advantages over singular learners
2) Decision Trees, Decision Forests
3) Data Sampling, Random Forests
4) Random Subspaces
5) Rotation forests
6) Learning With Successive Communities
7) Mixture of Experts
8) Combining Model Decisions, Two-Tier Collective Learning
9) Combining Model Decisions, Two-Tier Collective Learning
10) Error-correcting code-based methods
11) Kolektif öğrenmenin başarısını etkileyen faktörler
12) Collective learning applications in classification, clustering, regression
13) Methods Of Comparing Models
14) Meta Learning-1
15) Project Presentations
16) Project Presentations

Recommended or Required Reading

1- https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6407134
2- http://www.itc.ktu.lt/index.php/ITC/article/view/20935

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Modelling
6) Group Study
7) Lab / Workshop
8) Self Study
9) Problem Solving
10) Project Based Learning

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade	40%
Number Percentage Semester Studies Midterm Examination 1 30% Quiz 1 30% Presentation/Seminar 1 40%
Contribution of Final Examination to Course Grade	60%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required