ECTS - Practical Machine Learning
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 Lecturer(s) |
|
| 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;
|
| 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 | Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. | |||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. | |||||
| 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. | |||||
| 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. | |||||
| 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. | |||||
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 | ||