Practical Machine Learning (ISE441) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Practical Machine Learning	ISE441	7. Semester	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Compulsory Departmental Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)	Asst. Prof. Dr. Mehtap TUFAN
Course Assistants
Course Objectives	The aim of the course is to enable students, in the context of information systems engineering, to establish an end-to-end pipeline for data-driven problems, including data collection, preprocessing, visualization/analysis, modeling, and testing/evaluation; to select appropriate algorithms and performance metrics; to develop generalizable models using cross-validation and hyperparameter optimization; and to communicate findings effectively through technical reports and presentations. This aim is consistent with the official content headings of the course.
Course Learning Outcomes	The students who succeeded in this course; Acquire data from different sources and construct an appropriate dataset for analysis by evaluating data types and data quality. Conduct exploratory data analysis (EDA); diagnose variable relationships, outliers, and data issues using appropriate visualizations. Implement preprocessing (missing values, encoding, scaling) and feature engineering; justify data representation decisions affecting model performance. Build, train, and interpret basic supervised learning models for regression and classification problems. Examine data structure using unsupervised learning (clustering/dimensionality reduction) and discuss the suitability of the selected method in relation to the data and objectives. Compare models using cross-validation and hyperparameter search strategies; perform model selection while considering the risk of test data leakage. Select appropriate performance metrics; conduct error analysis and explain model strengths/weaknesses and the bias–variance trade-off. Design a reproducible end-to-end ML workflow (data–model–evaluation) for an information systems problem scenario and present outputs through a technical report/presentation with awareness of ethics and standards.
Course Content	Data collection, Data preprocessing, Data visualization and analysis, Feature engineering, Machine learning algorithms, Machine learning model selection and training, Model testing and evaluation, Hyperparameter optimization, Crossvalidation

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Course introduction; ML lifecycle; data–model–evaluation pipeline	An Introduction to Statistical Learning (Intro to Ch. 1)
2	Data sources; data collection; data quality; basic ethics/standards awareness	Intro to scikit-learn & pandas docs
3	Data preprocessing I: missing values, outliers, data types	pandas User Guide: missing data
4	EDA & visualization; variable relationships; intro to data leakage risks	scikit-learn: evaluation overview
5	Feature engineering; scaling & encoding; pipelines	scikit-learn: model selection & workflows
6	Supervised learning I: regression; error metrics; basic regularization	ISL: regression chapters
7	Supervised learning II: classification; confusion matrix; ROC–AUC, F1	ISL: classification chapters
8	Midterm exam; term project problem & dataset approval	Project guidelines (institutional)
9	Trees & ensembles (RF/boosting); overfitting discussion	ISL: trees/ensembles
10	Unsupervised learning: clustering; dimensionality reduction (PCA)	ISL: unsupervised learning
11	Model evaluation: cross-validation; leakage-aware design	scikit-learn cross-validation
12	Hyperparameter optimization: grid/random; nested ideas	scikit-learn hyperparameter tuning
13	Error analysis; metric selection; interpretability & report writing	Constructive alignment & reporting notes
14	Applied case: prediction/segmentation/anomaly in IS; pre-deployment checklist	Lecture notes + review
15	Project presentations; peer feedback; overall review	Project report template
16	Final Exam	ISL review + lecture notes

Sources

Course Book	1. An Introduction to Statistical Learning (official website provides access to R and Python editions)
Other Sources	2. The Elements of Statistical Learning (official author website provides access to the PDF)
	3. Introduction to Machine Learning (MIT Press book page)

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	3	10
Homework Assignments	4	20
Presentation	-	-
Project	1	25
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	20
Final Exam/Final Jury	1	25
Toplam	10	100

Percentage of Semester Work	50
Percentage of Final Work	50
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	Gains adequate knowledge in mathematics, science, and subjects specific to the software engineering discipline; acquires the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.
2	Gains the ability to identify, define, formulate, and solve complex engineering problems; selects and applies proper analysis and modeling techniques for this purpose.
3	Develops the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose.
4	Demonstrates the ability to select, and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; uses information technologies effectively.
5	Develops the ability to design experiments, gather data, analyze, and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline.
6	Demonstrates the ability to work effectively both individually and in disciplinary and interdisciplinary teams in fields related to software engineering.
7	Demonstrates the ability to communicate effectively in Turkish, both orally and in writing; to write effective reports and understand written reports, to prepare design and production reports, to deliver effective presentations, and to give and receive clear and understandable instructions.
8	Gains knowledge of at least one foreign language; acquires the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions.
9	Acquires an awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself.
10	Acts in accordance with ethical principles and possesses knowledge of professional and ethical responsibilities.
11	Knows the standards used in software engineering practices.
12	Knows about business practices such as project management, risk management and change management.
13	Gains awareness about entrepreneurship and innovation.
14	Gains knowledge on sustainable development.
15	Has knowledge about the universal and societal impacts of software engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering.
16	Acquires awareness of the legal consequences of engineering solutions.
17	Applies knowledge and skills in identifying user needs, developing user-focused solutions and improving user experience.
18	Gains the ability to apply engineering approaches in the development of software systems by carrying out analysis, design, implementation, verification, validation, and maintenance processes.

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	1	16
Presentation/Seminar Prepration
Project	1	10	10
Report
Homework Assignments	4	4	16
Quizzes/Studio Critics	3	2	6
Prepration of Midterm Exams/Midterm Jury	1	15	15
Prepration of Final Exams/Final Jury	1	20	20
Total Workload			131