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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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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
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Course Content
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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.
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Course Methods and Techniques
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Grading Policy
The final grades will be computed based on the general performance of the class and the distribution of grades (i.e. who deserves A and who deserves F). The grading strategy will be a combination of the standard catalogue grading and curve grading.
Attendance Policy
Each student is expected to attend to at least 50% of the theoretical classes. If not he/she will get NA as the final grade.
Late Submission Policy
It is the student s responsibility to follow the classes and do the assignments on time. Late submissions will be subject to a penalty of 25% if submitted within one week after the due date and %50 if submitted after one week.
Make-Up Policy
There are no make-ups in homework assignments, labs and quizzes. The student may be exempt from these assignments if a written and formal documentation is provided. Possible reasons for excused absences include serious illnesses, illness or death of a family member, university related trips and other serious circumstances. Acceptable documents for claiming an excused absence include medical doctor’s statements, petitions related to official university travels, court related documents, etc. If the student misses an exam (midterms or final) he or she can take a make-up exam upon submitting a formal document.
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Prerequisites and co-requisities
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None
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Course Coordinator
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Prof.Dr. VEHBİ ÇAĞRI GÜNGÖR cagri.gungor@agu.edu.tr
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Name of Lecturers
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Associate Prof.Dr. ZAFER AYDIN
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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How models work, basic data exploration (LO1)
Your first machine learning model, model validation (LO1, LO2, LO3, LO4)
Underfitting and overfitting (LO1, LO2, LO3, LO4)
Random forests (LO1, LO2, LO3, LO4)
Machine learning competitions (LO1, LO2, LO3, LO4)
Missing values (LO1, LO2, LO3, LO4)
Categorical variables (LO1, LO2, LO3, LO4)
Pipelines (LO1, LO2, LO3, LO4)
Cross-validation (LO1, LO2, LO3, LO4)
XGBoost (LO1, LO2, LO3, LO4)
Data leakage (LO1, LO2, LO3, LO4)
Hyper-parameter optimization (LO1, LO2, LO3, LO4)
Feature selection (LO1, LO2, LO3, LO4)
Feature engineering (LO1, LO2, LO3, LO4)
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