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 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 |
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 | Possesses sufficient knowledge in mathematics, natural sciences, and discipline-specific topics in Electrical and Electronics Engineering; uses this theoretical and practical knowledge to solve complex engineering problems. | X | ||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analytical and modeling methods for this purpose. | X | ||||
| 3 | Designs complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, depending on the nature of the design.) | X | ||||
| 4 | Selects and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications; effectively uses information technologies. | |||||
| 5 | Designs experiments, conducts tests, collects data, analyzes, and interprets results to investigate complex engineering problems or discipline-specific research topics. | |||||
| 6 | Works effectively in disciplinary and interdisciplinary teams; develops the ability to work independently. | X | ||||
| 7 | Communicates effectively in both written and verbal forms; possesses proficiency in at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear instructions. | |||||
| 8 | Recognizes the need for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself. | |||||
| 9 | Acts in accordance with ethical principles, assumes professional and ethical responsibility, and possesses knowledge about the standards used in engineering practices. | |||||
| 10 | Possesses knowledge about professional practices such as project management, risk management, and change management; gains awareness of entrepreneurship and innovation; understands the principles of sustainable development. | |||||
| 11 | Understands the universal and societal impacts of engineering practices on health, environment, and safety; recognizes the contemporary issues reflected in the field of engineering and understands the legal implications of engineering solutions. | |||||
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 | ||