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 | An ability to apply knowledge in mathematics and basic sciences and computational skills to solve manufacturing engineering problems | |||||
| 2 | An ability to define and analyze issues related with manufacturing technologies | |||||
| 3 | An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment | |||||
| 4 | An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints | |||||
| 5 | An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications | |||||
| 6 | An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly | |||||
| 7 | An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually | |||||
| 8 | An ability to attain efficient communication skills in Turkish and English both verbally and orally | |||||
| 9 | An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology | |||||
| 10 | An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering | |||||
| 11 | An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development | |||||
| 12 | An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process | |||||
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 | ||