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 Learn the techniques used for developing machine learning models Develop skills for practical aspects of machine learning Course Content This course provides an introduction to machine learning. The topics include basic probability, model selection, overfitting, curse of dimensionality, decision theory, linear models for regression, linear models for classification, kernel methods, dimension reduction and ensemble methods. Students will learn the concepts behind the algorithms by exploring the fundamental mathematical principles. Methods will be implemented by a software and applied on various machine learning problems. Course Methods and Techniques 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. Prerequisites and co-requisities None Course Coordinator Asist Prof.Dr. ZAFER AYDIN zafer.aydin@agu.edu.tr Name of Lecturers None Assistants None Work Placement(s) No Recommended or Required Reading Resources Introduction: polynomial curve fitting, probability theory, model selection, curse of dimensionality, decision theory (LO1, LO2) Linear models for regression (LO1, LO2, LO3, LO4, LO5) Linear models for classification (LO1, LO2, LO3, LO4, LO5) Kernel methods (LO1, LO2) Sparse kernel machines, support vector machines (LO1, LO2, LO3, LO4, LO5) Dimension reduction, PCA (LO1, LO2, LO3, LO4, LO5) Combining models, ensembles (LO1, LO2, LO3, LO4, LO5)

 





https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77826&lang=en