ECTS - Introduction to Machine Learning
Introduction to Machine Learning (CMPE363) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Introduction to Machine Learning | CMPE363 | Area Elective | 2 | 2 | 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 introduce Machine Learning concepts, algorithms, and their applications in practice, without requiring advanced calculus, linear algebra, and probability theory. |
| 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 Metrics and Scoring |
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 and Ensembles of Decision Trees | Ch. 2.5 Ch. 2.6 |
| 7 | Support Vector Machines | Ch. 2.7 |
| 8 | Unsupervised Learning | Ch. 3.1 |
| 9 | Data Transformations | Ch. 3.2 |
| 10 | Dimensionality Reduction: Principal Component Analysis (PCA) | Ch 3.3 |
| 11 | Feature Extraction | Ch. 3.4 |
| 12 | Clustering: K-means | Ch 3.5 |
| 13 | Model Evaluation: cross-validation, leave-one-out, grid search | Ch 5.1 |
| 14 | Evaluation Metrics and Scoring | Ch. 5.2 |
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. 1. Machine Learning 101, Data Science. Nov 26, 2018 |
| 3. 2. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems / Aurelien Geron. | |
| 4. 3. Introduction to Machine Learning, Ethem Alpaydin. MIT Press, 2014. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | 1 | 30 |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 1 | 10 |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 4 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| 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 | Attains knowledge through wide and in-depth investigations his/her field and surveys, evaluates, interprets, and applies the knowledge thus acquired. | |||||
| 2 | Has a critical and comprehensive knowledge of contemporary engineering techniques and methods of application. | |||||
| 3 | By using unfamiliar, ambiguous, or incompletely defined data, completes and utilizes the required knowledge by scientific methods; is able to fuse and make use of knowledge from different disciplines. | |||||
| 4 | Has the awareness of new and emerging technologies in his/her branch of engineering profession, studies and learns these when needed. | |||||
| 5 | Defines and formulates problems in his/her branch of engineering, develops methods of solution, and applies innovative methods of solution. | |||||
| 6 | Devises new and/or original ideas and methods; designs complex systems and processes and proposes innovative/alternative solutions for their design. | |||||
| 7 | Has the ability to design and conduct theoretical, experimental, and model-based investigations; is able to use judgment to solve complex problems that may be faced in this process. | |||||
| 8 | Functions effectively as a member or as a leader in teams that may be interdisciplinary, devises approaches of solving complex situations, can work independently and can assume responsibility. | |||||
| 9 | Has the oral and written communication skills in one foreign language at the B2 general level of European Language Portfolio. | |||||
| 10 | Can present the progress and the results of his investigations clearly and systematically in national or international contexts both orally and in writing. | |||||
| 11 | Knows social, environmental, health, safety, and legal dimensions of engineering applications as well as project management and business practices; and is aware of the limitations and the responsibilities these impose on engineering practices. | |||||
| 12 | Commits to social, scientific, and professional ethics during data acquisition, interpretation, and publication as well as in all professional activities. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 2 | 32 |
| Laboratory | 12 | 2 | 24 |
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 1 | 16 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 1 | 8 | 8 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 20 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 125 | ||