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 | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | |
| 15 | Review | |
| 16 | Review |
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 | 2. Machine Learning 101, Data Science. Nov 26, 2018 |
| 3. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems / Aurelien Geron | |
| 4. Introduction to Machine Learning, Ethem Alpaydin. MIT Press, 2014. | |
| 5. Orange web page, 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 advanced knowledge of computing and/or informatics to solve software engineering problems. | |||||
| 2 | Develop solutions using different technologies, software architectures and life-cycle approaches. | |||||
| 3 | An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. | |||||
| 4 | An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | |||||
| 5 | Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | |||||
| 6 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | |||||
| 7 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | |||||
| 9 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | |||||
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 | ||