Course Information

Course Title	Code	Semester	L+U Hour	Credits	ECTS
MACHINE LEARNING WITH PYTHON	803400815020		3 + 0	3.0	10.0

Prerequisites

None

Language of Instruction	English
Course Level	Graduate Degree
Course Type	Compulsory
Mode of delivery
Course Coordinator
Instructors	Bahadır AKTUĞ
Assistants
Goals	The aim of this course is to help students gain the necessary skills to apply machine learning techniques with the tools provided by Python libraries.
Course Content	Overview Python language (data types and variables, lists, tuples, dictionaries, sets, loops, logical operators and flow control, exception handling, file input/output, standard input and output, command line arguments, iterators and generators), Numpy, Scipy, Pandas, Scikit-learn, Supervised Machine Learning Algorithms, Deep Learning (Neural Networks), Naïve Bayes Classifiers, Decision Trees, Unsupervised Learning, Principal Component Analysis, Clustering, Model Evaluation, Cross-Validation.
Learning Outcomes	1) Supervised Machine Learning 2) Deep Learning 3) Bayes Classifiers

Week	Topics	Teaching and Learning Methods and Techniques	Study Materials
1. Week	Data types and variables	Lecture	Homework
2. Week	Fundamentals of Object oriented programming	Lecture	Homework
3. Week	Data types in Python (list, tuple, dictionary, set)	Lecture	Homework
4. Week	Basic functions and modules in Python	Lecture	Homework
5. Week	Loops, iterators and generators, logical comparison	Lecture	Homework
6. Week	File input and output, command line arguments	Lecture	Homework
7. Week	Numpy, Scipy	Lecture	Homework
8. Week	Pandas, Matplotlib	Lecture	Homework
9. Week	Gözetimli Makine Öğrenmesi	Lecture	Homework
10. Week	Deep Learning	Lecture	Homework
11. Week	Bayes Classifiers	Lecture	Homework
12. Week	Unsupervised Learning	Lecture	Homework
13. Week	Principal Component Analysis	Lecture	Homework
14. Week	Cross Validation	Lecture	Homework

Recommended Sources

Pilgrim, M. (2014). Dive into Python 3 by. Free online version: DiveIntoPython3.org ISBN: 978-1430224150.

Summerfield, M. (2014) Programming in Python 3 2nd ed (PIP3) : - Addison Wesley ISBN: 0-321-68056-1.

Wentworth, P., Elkner, J., Downey, A.B., Meyers, C. (2014). How to Think Like a Computer Scientist: Learning with Python

*DK = Course's Contrubution.

	0	1	2	3	4	5
Level of contribution	None	Very Low	Low	Fair	High	Very High

Event	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Total weeks*Hours per week)	14	3	42
Work Hour outside Classroom (Preparation, strengthening)	14	9	126
Presentation (Including Preparation Time)	1	20	20
Project (Including Preparation and presentation Time)	1	5	5
Report (Including Preparation and presentation Time)	1	1	1
Activity (Web Search, Library Work, Trip, Observation, Interview etc.)	1	1	1
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)	1	1	1
Seminar	1	20	20
Quiz	1	10	10
Time to prepare for Quiz	1	20	20
Midterm Exam	1	1	1
Final Exam	1	1	1
Time to prepare for Final Exam	1	40	40
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course			30