ECTS - Machine Learning for Engineers

Machine Learning for Engineers (CMPE468) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Machine Learning for Engineers CMPE468 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The course objective is to provide an introduction to Machine Learning concepts, algorithms, and their applications in engineering areas without requiring advanced calculus, linear algebra, and probability theory, and the ability to work within interdisciplinary teams for developing a project for which the teams will be formed from different disciplines.
Course Learning Outcomes The students who succeeded in this course;
  • Describe fundamental concepts and algorithms of machine learning and their applications
  • Evaluate the machine learning models and parameter tuning
  • Apply machine learning algorithms to particular engineering applications
  • Work within interdisciplinary teams for developing a project
Course Content Artificial intelligence, machine learning, supervised and unsupervised learning, binary classification, multiclass classification, regression, clustering, model evaluation.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Why Machine Learning? A First Application: Classifying Iris Species Ch.1
2 Supervised Learning: Classification and Regression Ch. 2.1
3 k-Nearest Neighbors Ch. 2.2
4 Linear Models Ch. 2.3
5 Naive Bayes Classifiers Ch. 2.4
6 Decision Trees Ch. 2.5
7 Random Trees Ch. 2.6
8 Support Vector Machines Ch. 2.7
9 Unsupervised Learning Ch. 3.1
10 Clustering: K-means Ch. 3.5
11 Model Evaluation: cross-validation, leave-one-out, grid search Ch 5.1
12 Evaluation Metrics and Scoring Ch. 5.2
13 Project Presentations
14 Project Presentations

Sources

Course Book 1. Introduction to Machine Learning with Python, A Guide for Data Scientists by Andreas C. Müller and Sarah Guido, O’Reilly Media, Inc, October 2016
Other Sources 3. Machine Learning 101, Data Science. Nov 26, 2018
4. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems / Aurelien Geron.
5. Introduction to Machine Learning, Ethem Alpaydin. MIT Press, 2014.
6. Orange web site, https://orange.biolab.si/

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge of mathematics, science, and engineering.
2 An ability to design and conduct experiments, as well as to analyze and interpret data.
3 An ability to design a system, component, or process to meet desired needs.
4 An ability to function on multi-disciplinary teams.
5 An ability to identify, formulate, and solve engineering problems.
6 An understanding of professional and ethical responsibility.
7 An ability to communicate effectively.
8 The broad education necessary to understand the impact of engineering solutions in a global and societal context.
9 Recognition of the need for, and an ability to engage in life-long learning.
10 Knowledge of contemporary issues.
11 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
12 Skills in project management and recognition of international standards and methodologies

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 1 10 10
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 15 15
Total Workload 115