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 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
1 2 3 4 5
1 Gain sufficient knowledge in mathematics, science and computing; be able to use theoretical and applied knowledge in these areas to solve engineering problems related to information systems.
2 To be able to identify, define, formulate and solve complex engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.
3 Designs a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose.
4 To be able to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in information systems engineering applications; to be able to use information technologies effectively.
5 Designs and conducts experiments, collects data, analyzes and interprets results to investigate complex engineering problems or research topics specific to the discipline of information systems engineering.
6 Can work effectively in disciplinary and multidisciplinary teams; can work individually.
7 a. Communicates effectively 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. b. Knows at least one foreign language.
8 To be aware of the necessity of lifelong learning; to be able to access information, to be able to follow developments in science and technology and to be able to renew himself/herself continuously.
9 a. Acts in accordance with the principles of ethics, gains awareness of professional and ethical responsibility. b. Gains knowledge about the standards used in information systems engineering applications.
10 a. Gains knowledge about business life practices such as project management, risk management and change management. b. Gains awareness about entrepreneurship and innovation. c. Gains knowledge about sustainable development.
11 a. To be able to acquire knowledge about the universal and social effects of information systems engineering applications on health, environment and safety and the problems of the era reflected in the field of engineering. b. Gains awareness of the legal consequences of engineering solutions.

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