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
#	Program Qualifications / Competencies	1	2	3	4	5
1	Knowledge of mathematics, natural sciences, engineering fundamentals, computing, and topics specific to the relevant engineering discipline; the ability to use this knowledge in the solution of complex engineering problems.
2	The ability to identify, formulate, and analyze complex engineering problems using knowledge of basic sciences, mathematics, and engineering, and considering the UN Sustainable Development Goals relevant to the problem.
3	The ability to design creative solutions for complex engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, considering realistic constraints and conditions.
4	The ability to select and use appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling, for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5	The ability to use research methods for the investigation of complex engineering problems, including literature search, designing and conducting experiments, collecting data, and analyzing and interpreting results.
6	Knowledge of the effects of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7	Acting in accordance with engineering professional principles, knowledge of ethical responsibility; awareness of acting impartially without discrimination on any grounds and being inclusive of diversity.
8	The ability to work effectively individually and in intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid) as a team member or leader.
9	"The ability to communicate effectively orally and in writing on technical topics, considering the various differences of the target audience (such as education, language, profession).
10	Knowledge of practices in business life such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11	The ability to engage in life-long learning, including independent and continuous learning, adapting to new and emerging technologies, and thinking inquisitively regarding technological changes.

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