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
#	Program Qualifications / Competencies	1	2	3	4	5
1	Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.					X
2	The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose.					X
3	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose.	X
4	The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively.		X
5	The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline.
6	The ability to work effectively in inter/inner disciplinary teams; ability to work individually
7	Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
8	The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
9	Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology.
10	The ability to behave according to ethical principles, awareness of professional and ethical responsibility;
11	Knowledge of the standards utilized in software engineering applications
12	Knowledge on business practices such as project management, risk management and change management;
13	Awareness about entrepreneurship, innovation
14	Knowledge on sustainable development
15	Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety;
16	Awareness of the legal consequences of engineering solutions
17	An ability to describe, analyze and design digital computing and representation systems.
18	An ability to use appropriate computer engineering concepts and programming languages in solving computing 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