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)
N/A
Corequisite(s)
N/A
Special Requisite(s)
Basic programming knowledge (Preferably python)
Instructor(s)
Lecturer Dr. Tevfik Uyar, Arş. Gör. Pınar Sarp
Course Assistant(s)
Schedule
Friday, 11:00-13:00
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
midterm
exam
9. Week
midterm
exam
10. Week
Decision Trees and Ensembling Methods (CART, RF and GBC)
computer aided application
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
computer aided application
15. Week
Deep learning and Artificial Neural Networks-2
computer aided application
16. Week
final
exam
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Program Outcomes
PO-1
Demonstrates a basic level of understanding in related disciplines (such as economics, sociology, psychology, quantitative sciences, etc.) that form a foundation for business administration, and makes use of and applies them to the field of business.
PO-2
Applies mathematical, scientific and social knowledge to business problems.
PO-3
Demonstrates a basic level of understanding in business functions and management (such as management, production, marketing, accounting, finance, human resources, behavioural sciences, etc.) and interprets the theoretical arguments focusing on interactions between the actors and the cultures in the field.
PO-4
Determines how to use acquired theoretical and practical knowledge and skills related to business in application and field analysis and applies them.
PO-5
Identifies and evaluates the relations in the field of business; describes the problems and presents analytical solutions through modelling and interpreting (critical thinking).
PO-6
Designs a business process in any functional stage that complies with identified objectives.
PO-7
Develops effective business communication skills (written-verbal/formal-informal).
PO-8
Owns effective working skills individually or on a team in business and multidisciplinary fields.
PO-9
Acts with a sense of professional and ethical responsibility.
PO-10
Improves effective verbal and written communication skills in Turkish, and acquires competence in minimum one foreign language.
