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	Adequate knowledge in mathematics, science and subjects specific to the aerospace engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.
2	The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose.
3	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose.
4	The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in aerospace engineering applications; the ability to utilize information technologies effectively.
5	The ability to design experiments and their setups, to make experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the aerospace engineering discipline.
6	The ability to work effectively in inter/inner disciplinary teams; ability to work individually.
7	Effective oral and written communication skills in Turkish; the knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8	Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development
9	The ability to behave according to ethical principles, awareness of professional and ethical responsibility; knowledge of the standards utilized in aerospace engineering applications.
10	Knowledge on business practices such as project management, risk management and change management; awareness about entrepreneurship, innovation; knowledge on sustainable development.
11	Knowledge on the effects of aerospace engineering applications on the universal and social dimensions of health, environment and safety; awareness of the legal consequences of engineering solutions.
12	Knowledge on aerodynamics, materials used in aerospace engineering, structures, propulsion, flight mechanics, stability and control, and an ability to apply these on aerospace engineering problems.
13	Knowledge on orbit mechanics, position determination, telecommunication, space structures and rocket propulsion.

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