ECTS - Practical Machine Learning

Atılım University - ECTS INFORMATION GUIDE

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 Natural & Applied Sciences Master's Degree
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
1 2 3 4 5
1 An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems.
2 Develop solutions using different technologies, software architectures and life-cycle approaches.
3 An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices.
4 An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5 Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects.
6 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
7 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
9 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

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