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	Gains sufficient knowledge in subjects specific to mathematics, natural sciences, and engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.
2	Defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose.
3	Designs a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods.
4	Selects and uses modern techniques and tools necessary for analyzing and solving complex problems encountered in engineering applications; gains the ability to use information technologies effectively.
5	Designs experiments, conducts experiments, collects data, and analyzes and interprets the results for studying complex engineering problems or research topics specific to engineering disciplines.
6	Works effectively in both disciplinary and multidisciplinary teams; gains the ability to work individually.
7	Develops effective oral and written communication skills; acquires proficiency in at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear and understandable instructions.
8	Develops awareness of the necessity of lifelong learning; gains access to information, follows developments in science and technology, and continuously renews oneself.
9	Acts in accordance with ethical principles, takes professional and ethical responsibility, and possesses knowledge of standards used in engineering applications.
10	Gains knowledge of business practices such as project management, risk management, and change management; develops awareness of entrepreneurship and innovation; possesses knowledge of sustainable development.
11	Gains knowledge of the impacts of engineering applications on health, environment, and safety in universal and societal dimensions, and the issues reflected in contemporary engineering fields; develops awareness of the legal consequences of engineering solutions.
12	Gains the ability to work in both thermal and mechanical systems fields, including the design and implementation of such systems.

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