| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Acquire the knowledge and skills needed to use information systems and artificial intelligence applications aligned with the strategic goals of businesses.
- Understand the main fields of big data, data mining, machine learning (ML), and deep learning, including their differences, purposes, and methods.
- Will be able to know and apply the components of ML, along with its mathematical and statistical foundations.
- Become familiar with the general application steps of ML and use them to solve various types of problems.
- Implement the general application steps of ML within the framework of project management discipline.
- Gain knowledge of machine learning software and application development environments, and utilize them to address various problems.
- Understand and apply supervised machine learning models and algorithms.
- Understand and apply unsupervised learning models and algorithms.
- Understand and apply image processing learning models and algorithms.
- Understand and apply text and natural language processing learning models and algorithms.
- Undertake an ML term project to address a business-related issue, executing ML analysis, design, and implementation procedures, and generating the required outcomes.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | The aim of this course is to introduce various techniques that facilitate learning from data by utilizing basic and advanced concepts of machine learning. This course provides comparisons and applications of different techniques and algorithms, addressing the question, "How can we learn from past experiences?" |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to artificial intelligence and fundamental concepts; application areas; impacts on personal, social, and business domains; data mining; fundamental components of machine learning (ML), and the mathematical and statistical foundations of ML |
|
2nd Week |
The objectives of information systems usage in businesses, their impacts on organizations, the competitive forces model, the business value chain and network models, the Orange ML and data mining software environment |
|
3rd Week |
The Orange ML and data mining software environment, general implementation steps of machine learning projects: demonstration of the general application steps for logistic regression and random forest models |
|
4th Week |
Statistical foundations of machine learning, basic statistical concepts; statistical applications in Orange ML, and data mining software environment |
|
5th Week |
Mathematical foundations of machine learning, matrices and matrix operations, application of matrix operations in machine learning |
|
6th Week |
Supervised machine learning models: linear and multivariable regression model |
|
7th Week |
Supervised machine learning models: decision trees model |
|
8th Week |
Midterm exam |
|
9th Week |
Supervised machine learning models: random forest model |
|
10th Week |
Supervised/unsupervised machine learning |
|
11th Week |
Supervised/unsupervised machine learning |
|
12th Week |
Image processing learning models |
|
13th Week |
Text and natural language processing learning models |
|
14th Week |
General review and evaluation of term projects |
|
| Recommended or Required Reading | Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: A guide for data scientists. O'Reilly Media.
Theobald, O. (2018). Machine learning for absolute beginners: A plain English introduction (Machine learning from scratch). Independent Publisher.
Mueller, J. P., & Massaron, L. (2016). Machine learning for dummies. John Wiley & Sons, Inc.
Singh, P. (2019). Machine learning with PySpark. APress Media.
Mathur, P. (2019). Machine learning applications using Python: Case studies from healthcare, retail, and finance. APress Media.
Hassanien, E. (2019). Machine learning paradigms: Theory and application. Springer.
Wodecki, A. (2019). Artificial intelligence in value creation. Palgrave Macmillan.
Ertel, W. (2017). Introduction to artificial intelligence. Springer-Verlag.
Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall. |
|---|
| Planned Learning Activities and Teaching Methods |
Project-Supported Traditional lnstructional Method
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
40 |
| Project |
1 |
20 |
| Other |
1 |
10 |
| Total(%) |
|
70 |
| Contribution of In-term Studies to Overall Grade(%) |
|
70 |
| Contribution of Final Examination to Overall Grade(%) |
|
30 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
3 |
42 |
| Preparation for Final exam |
1 |
22 |
22 |
| Course hours |
13 |
1 |
13 |
| Preparation for Midterm exam |
1 |
14 |
14 |
| Laboratory (including preparation) |
13 |
2 |
26 |
| Final exam |
1 |
1.5 |
1.5 |
| Homework |
|
|
|
| Presentation (including preperation) |
1 |
3 |
3 |
| Project |
1 |
3 |
3 |
| Term Project Research |
1 |
8 |
8 |
| Report writing |
1 |
8 |
8 |
| Total Work Load | 141.5 |
| Total Work Load / 30 (h) | 4.71 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
