Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1ECE564MACHINE LEARNING IN PYTHON3+0+037,514.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program ELECTRICAL AND COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Develop knowledge for the fundamentals of machine learning
Develop knowledge for the machine learning models, the problem domains they are used for, and their practical aspects
Develop skills for implementing machine learning models using libraries of Python
Course Content This course introduces machine learning using Python programming language. It covers data preprocessing, visualization, classification, regression, model training, evaluation and fine-tuning. Students will get hands-on practical knowledge implementing machine learning models and applying them to various learning problems.
Course Methods and Techniques In this course, lectures, sample applications and live coding sessions will be used as the basic method. Applied studies will be done with Python programming language and libraries such as scikit-learn, pandas, matplotlib. Students will reinforce machine learning algorithms by applying them through individual assignments and small projects.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Zafer Aydın Avesis zafer.aydin@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources 1. Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow Concepts Tools and Techniques to Build Intelligent Systems, Aurelien Geron, O’Reilly, 2019. 2. Python Data Science Handbook: Essential Tools for Working with Data, Jake Vanderplas, O’Reilly, 2016. 3. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition, Sebastian Raschka, Vahid Mirjalili, Packt Publishing, 2019. 4. Introduction to Machine Learning with Python: A Guide for Data Scientists, Andreas C. Müller, Sarah Guido, O’Reilly, 2016.
Course Notes 1. Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow Concepts Tools and Techniques to Build Intelligent Systems, Aurelien Geron, O’Reilly, 2019.
2. Python Data Science Handbook: Essential Tools for Working with Data, Jake Vanderplas, O’Reilly, 2016.
3. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition, Sebastian Raschka, Vahid Mirjalili, Packt Publishing, 2019.
4. Introduction to Machine Learning with Python: A Guide for Data Scientists, Andreas C. Müller, Sarah Guido, O’Reilly, 2016.
Documents Will be shared on Canvas
Assignments Will be shared on Canvas
Exams Will be shared on Canvas

Course Category
Mathematics and Basic Sciences %20
Engineering %20
Engineering Design %20
Social Sciences %0
Education %0
Science %20
Health %0
Field %20

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı 1 % 20
Quiz/Küçük Sınav 1 % 10
Ödev 1 % 25
Proje/Çizim 1 % 25
Final examination 1 % 20
Total
5
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Grup Projesi 1 35 35
Ev Ödevi 3 7 21
Sunum için Hazırlık 5 2 10
Senkron Ders 14 3 42
Ders Dışı Ara Sınav 1 40 40
Final Sınavı 1 35 35
Total Work Load   Number of ECTS Credits 7,5 183

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain fundamental techniques of machine learning methods
2 Implement machine learning models using libraries of Python including scikit-learn
3 Perform simulations and experiments to train, optimize and evaluate machine learning models on real data sets
4 Apply the appropriate techniques and models to solve machine learning problems

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 The machine learning landscape: what is machine learning, types of machine learning systems - -
2 The machine learning landscape: challenges of machine learning, testing and validation - -
3 End-to-end machine learning project: visualize data, prepare data - -
4 End-to-end machine learning project: train a model, cross-validation - -
5 End-to-end machine learning project: fine-tune your model - -
6 Classification: binary classification, performance measures - -
7 Classification: multi-class classification - -
8 Semester break - -
9 Midterm Exam - -
10 Training models: linear regression, gradient descent Activity: homework 3, reading chapter 4 from Geron’s book, tutorials - -
11 Training models: polynomial regression, learning curves Activity: quiz 4, project, reading chapter 4 from Geron’s book - -
12 Training models: regularized linear models, logistic regression Activity: quiz 5, project, reading chapter 4 from Geron’s book - -
13 Support vector machines: linear SVM, non-linear SVM Activity: project, reading chapter 5 from Geron’s book - -
14 Support vector machines: SVM regression, under the hood Activity: project, reading chapter 5 from Geron’s book - -

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 4 5 4 4 3 3 4 1 2 2 3
C1 3 5 3 4 3 2 3 1 2 2 3
C2 3 5 3 4 3 3 3 1 2 2 3
C3 4 4 4 4 3 3 4 1 2 2 3
C4 4 5 4 4 4 4 4 1 2 2 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77745&lang=en
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