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 of mathematics, science, and engineering. | |||||
| 2 | An ability to design and conduct experiments, as well as to analyze and interpret data. | |||||
| 3 | An ability to design a system, component, or process to meet desired needs. | |||||
| 4 | An ability to function on multi-disciplinary teams. | |||||
| 5 | An ability to identify, formulate, and solve engineering problems. | |||||
| 6 | An understanding of professional and ethical responsibility. | |||||
| 7 | An ability to communicate effectively. | |||||
| 8 | The broad education necessary to understand the impact of engineering solutions in a global and societal context. | |||||
| 9 | Recognition of the need for, and an ability to engage in life-long learning. | |||||
| 10 | Knowledge of contemporary issues. | |||||
| 11 | An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. | |||||
| 12 | Skills in project management and recognition of international standards and methodologies | |||||
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 | ||