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 Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • Describe fundamental concepts of machine learning and its applications
  • Evaluate the machine learning models and parameter tuning
  • Apply machine learning algorithms to particular applications
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 Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. X
2 Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. X
3 Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose. X
4 Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively. X
5 Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering.
6 Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually.
7 Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
8 Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
9 Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself.
10 Acts in accordance with ethical principles and has awareness of professional and ethical responsibility.
11 Has knowledge about the standards used in computer engineering applications.
12 Has knowledge about workplace practices such as project management, risk management, and change management.
13 Gains awareness about entrepreneurship and innovation.
14 Has knowledge about sustainable development.
15 Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering.
16 Gains awareness of the legal consequences of engineering solutions.
17 Analyzes, designs, and expresses numerical computation and digital representation systems.
18 Uses programming languages and appropriate computer engineering concepts to solve computational problems. X

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