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| Course Goals |
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 |
| 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 |
|
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