COURSE UNIT TITLE

: MACHINE LEARNING ALGORITHMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EKO 6058 MACHINE LEARNING ALGORITHMS ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR MURAT TANIK

Offered to

Econometrics

Course Objective

The aim of this lecture is to introduce machine learning algorithms. Supervised and unsupervised learning algorithms are explained with computer solutions

Learning Outcomes of the Course Unit

1   Learning basic concepts about machine learning algorithms
2   Understanding applications of optimization algorithms in machine learning
3   Learning supervised learning concept, theoretical basics and applications
4   Learning unsupervised learning concept, theoretical basics and applications
5   Applying suitable methods on the real-world data

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Machine learning and basic concepts
2 Optimization algorithms: Gradient descent and Mewton Algorithms
3 Evaluating learning algorithms: Model selection and train/validation/test sets
4 Bias/Variance and Learning Curves
5 Supervised Learning ans Classification Algorithms
6 Support Vector Machine, Kernel Functions
7 Nearest Neighbor Algorithms
8 C5.0 Classification Algorithms
9 Decision Trees and Application
10 Ensemble Learning Algorithms
11 Boosting Algorithms: AdaBoost, Gradient Boosting (GBM), Extreme-Gradient Boosting (XGBoost)
12 Bagging Algorithms: Bagging meta-estimator, Random Forest
13 Optimization based meta-learning and Multiobjective Optimization
14 Application Examples

Recomended or Required Reading

Smola, Alex, and S. V. N. Vishwanathan. "Introduction to machine learning." Cambridge University, UK 32.34 (2008): 2008.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
David, S., and Shai Shalev Shwartz. "Understanding Machine Learning: From Theory to Algorithms." Understanding Machine Learning: From Theory to Algorithms (2014).
Alpaydin, Ethem. Machine learning: the new AI. MIT press, 2016.

Planned Learning Activities and Teaching Methods

Lecture Method, Question-Answer Method, Discussion Method and Problem Solving Method - Applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 + STT * 0.20 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + STT * 0.20 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
TOTAL WORKLOAD (hours) 0

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.1222222111
LO.2222222222
LO.3222221112
LO.4222222222
LO.5222222222

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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