EE418 Introduction to Machine Learning

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
EE418 Introduction to Machine Learning 3 2 6
Prerequisite None It is a prerequisite to

None

Lecturer Kanita Karadžuzović Hadžiabdić Office Hours / Room / Phone
Tuesday:
14:00-16:00
Wednesday:
12:00-15:00
B F3.13 - 033 957 218
E-mail kanita@ius.edu.ba
Assistant Sen. Assist. Nesibe Merve Demir Assistant E-mail ndemir@ius.edu.ba
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, S
Additional Literature
  • Supplementary book: Introduction to Data Mining, Pang-ning Tan, Michael Steinbach and Vipin Kumar, Addison-Wesley, 2006
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 Face-to-face Teaching Method Delivery Notes
    WEEK TOPIC REFERENCE
    Week 1 Introduction to the course
    Week 2 Introduction to machine learning 1 + class notes
    Week 3 Understanding and preparing the data, Introduction to R 2 + class notes
    Week 4 Classification basic concepts and K-nn algorithm; Introduction to the Project 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 Clustering 9
    Week 13 Association analysis (introduction); Project discussion 8
    Week 14 Project Presentations
    Week 15 Review for the final
    Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
    Final Exam 1 35 1,2,3,4
    Semester Evaluation Components
    Homeworks 3 15 1,2,3,4
    Midterm 1 30 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 = 150
    *T= Teaching, P= Practice ECTS Credit = 6
    Course Academic Quality Assurance: Semester Student Survey Last Update Date: 03/12/2021
    QR Code for https://ecampus.ius.edu.ba/syllabus/ee418-introduction-machine-learning

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