To teach fundamentals of machine learning and artificial intelligence and provide students with capability of using this knowledge for pattern recognition.
Students completing this course successfully;
I. Will know the differences between shallow/deep, parametric/non-parametric, supervised/unsupervised machine learning algorithms.
II. will know theoretical base of frequently used algorithms and will be able to use them in applications
III. will be able to choose the appropriate algorithm according to the aim and data type.
IV. will be able to perform shallow and deep learning algorithm applications by Python scikitlearn libraries.
Prerequisite(s)
None
Corequisite(s)
None
Special Requisite(s)
Basic programming knowledge (Preferably python)
Instructor(s)
Lecturer Dr. Tevfik Uyar
Course Assistant(s)
Schedule
Wednesday 15:00-17:00, BK L-04
Office Hour(s)
Friday, after 15:00
Teaching Methods and Techniques
Applications
Principle Sources
E. Alpaydın (2011), Yapay Öğrenme, Boğaziçi Üniversitesi Yayınları, ISBN: 9786054238491
A.C.Müller, S.Guido (2016), Introduction to Machine Learning with Python, O’Reilly, ISBN: 9781449369415
Other Sources
Lecture notes & applications
www.veridefteri.com
Course Schedules
Week
Contents
Learning Methods
1. Week
Fundementals of machine learining, working with data and pre-processing
disclosure
2. Week
Python Anaconda distribution, Jupyter usage and introduction to required libraries
disclosure
3. Week
Linear regression and multiple regression
computer aided application
4. Week
kNN, feature selection and classification performance
computer aided application
5. Week
Navie Bayes classification
computer aided application
6. Week
Logistic Regression
computer aided application
7. Week
Support Vector Machines
computer aided application
8. Week
Decision Trees and Ensembling Methods (CART, RF and GBC)
computer aided application
9. Week
Midterm Exam Week
Assessment
10. Week
Midterm Exam Week
Assessment
11. Week
Dimension reduction and Principle Component Analysis
computer aided application
12. Week
Homework Presentations
Student presentations
13. Week
Unsupervised Learning and Clustering
computer aided application
14. Week
Deep learning and Artificial Neural Networks-1, Deep learning and Artificial Neural Networks-2
computer aided application
15. Week
Final Exam Week
Assessment
16. Week
Final Exam Week
Assessment
17. Week
Final Exam Week
Assessment
Assessments
Evaluation tools
Quantity
Weight(%)
Homework / Term Projects / Presentations
1
50
Final Exam
1
50
Program Outcomes
PO-1
To define his/her competencies using the theoretical and practical knowledge he/she acquired in the field of Economics and to use these competencies in practice.
PO-2
To discuss concepts and ideas in the field of Economics with scientific methods, to develop hypotheses, to interpret and evaluate the data obtained.
PO-3
To develop solution suggestions for complex and/or unpredictable problems encountered in practice, to report and present these suggestions in accordance with the academic publication rules.
PO-4
To identify regional and global issues/problems, to perform analyses based on scientific data and research, and to develop solution suggestions.
PO-5
To determine the learning needs in the field of Economics, to evaluate the acquired knowledge and skills with a critical approach, and to use this knowledge and skills to develop economic policies.
PO-6
To inform relevant people and institutions on issues related to the field of Economics and to gain the ability to convey written and verbal solutions to problems.
PO-7
To convey his/her thoughts and suggestions in the field of Economics to experts and non-experts by supporting them with quantitative and qualitative data, to discuss and to contribute to the development of new policies by revising suggestions according to the feedback he/she receives.
PO-8
To benefit from other disciplines that form the basis of the field of Economics, to develop multidisciplinary approaches by associating these disciplines with his/her knowledge in the field of Economics, to produce project and publications.
PO-9
To define the differences and relationships between classical and modern theories of economics and to observe the distinctions between classical and modern economic policies and to evaluate the compatibility of these policies with economic and social issues.
PO-10
To read and comprehend foreing news sources about economics being fluent in a foreign language, to scan the economic literature and to follow the most current approaches in this field.
PO-11
To take into account scientific and ethical values in the stages of collecting, interpreting and announcing economic data and carrying out statistical/econometric studies using these data, and also to carry out the publication process in accordance with academic publishing principles.
