EE418 Introduction to Machine Learning

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
Wed: 9:00 - 12:00
Prerequisite None It is a prerequisite to


Lecturer Office Hours / Room / Phone

Currently not available

Assistant Sen. Assist. Nesibe Merve Demir Assistant E-mail
Course Objectives To understand the basic prinicples of machine learning. To understand an manage the data. To select and apply machine learning methods appropriate to the task at hand. Performance evaluation of machine learning models.
Textbook Machine Learning with R, Brett Lantz, Packt Publishing, 2013, Supplementary book: Introduction to Data Mining, Pang-ning Tan, Michael Steinbach and Vipin Kumar, Addison-Wesley, 2006
Additional Literature
Learning Outcomes After successful  completion of the course, the student will be able to:
    Teaching Methods Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress.
    Teaching Method Delivery Teaching Method Delivery Notes
    Week 1 Introduction to the course
    Week 2 Introduction to machine learning 1
    Week 3 Understanding and preparing the data 2
    Week 4 Classification basic concepts and K-nn algorithm 3
    Week 5 Naïve bayes algorithm 4
    Week 6 Decision trees and rule-based classifier 5
    Week 7 MIDTERM
    Week 8 Performance evaluation, class imbalance problem 10
    Week 9 Ensemble methods 11
    Week 10 Regression 6
    Week 11 Artificial neural networks, support vector machines 7
    Week 12 Association analysis 8
    Week 13 Clustering 9
    Week 14 Project Presentations
    Week 15 Project Presentations
    Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
    Final Exam 1 40 1,2,3,4
    Semester Evaluation Compenents
    Homeworks 3 15 1,2,3,4
    Midterm 1 25 1,2,3,4
    Participation 1 5 1,2,3,4
    Project 1 20 1,2,3,4,5
    ***     ECTS Credit Calculation     ***
     Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
    Lecture Hours 3 15 45 Labs 2 14 28
    Home Study 2 15 30 Project 3 10 30
    Midterm exam study 8 1 8 Final exam study 9 1 9
            Total Workload Hours =
    *T= Teaching, P= Practice ECTS Credit =
    Course Academic Quality Assurance: Semester Student Survey Last Update Date: 15/02/2021
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