Information Package / Course Catalogue
| Course Code | : BPR113 |
| Course Type | : Required |
| Couse Group | : Short Cycle (Associate's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 1 |
| Prt. | : 1 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 3 |
The objective of this course is to enable students to learn the fundamental concepts of artificial intelligence and automation technologies, identify their applications across various sectors, and effectively use AI-powered tools and automation systems that are widely used today. Throughout the course, students will gain a basic understanding of artificial intelligence, machine learning, generative AI, robotic process automation (RPA), data analysis, and digital transformation; develop applications for automating business processes; and evaluate the ethical, security, and societal impacts of AI technologies.
The concepts of artificial intelligence and automation, their historical development, and key components. An introduction to machine learning and deep learning. Generative AI systems and large language models. AI-powered content creation, data analysis, and decision support systems. Robotic Process Automation (RPA) and the automation of business processes. Data usage and data quality in artificial intelligence applications. Fundamental principles of image processing, natural language processing, and speech technologies. Artificial intelligence applications in the industrial, education, healthcare, finance, and service sectors. Creating workflows and developing automation scenarios using artificial intelligence tools. AI ethics, data privacy, security, and legal regulations. The role of AI and automation in the future of the workplace. Applied exercises and project development process.
| 1. | It explains the basic concepts related to artificial intelligence, machine learning, generative AI, and automation technologies. |
| 2. | It uses artificial intelligence tools to generate digital content such as text, images, presentations, and reports. |
| 3. | It uses effective prompt-generation techniques to obtain efficient results from AI systems. |
| 4. | It creates basic automation processes and workflows using no-code/low-code tools. |
| 5. | Evaluates artificial intelligence and automation applications in light of ethical, security, and data privacy principles. |
| 1. | Artificial Intelligence and Machine Learning Mitat Uysal |
| Type of Assessment | Count | Percent |
|---|---|---|
| Assignment | 1 | %10 |
| Quiz | 1 | %10 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 0 | 1 | 14 |
| Lecture - Practice | 14 | 0 | 1 | 14 |
| Assignment | 1 | 9 | 1 | 10 |
| Quiz | 1 | 9 | 1 | 10 |
| Midterm Examination | 1 | 10 | 1 | 11 |
| Final Examination | 1 | 15 | 1 | 16 |
| TOTAL WORKLOAD (hours) | 75 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | |
OÇ-1 | 3 | 3 | 3 | 2 | 4 | 3 | 3 | 4 | 4 | 5 | 4 |
OÇ-2 | 3 | 4 | 3 | 2 | 4 | 3 | 4 | 4 | 3 | 4 | 5 |
OÇ-3 | 3 | 3 | 4 | 2 | 4 | 3 | 5 | 5 | 3 | 4 | 5 |
OÇ-4 | 3 | 3 | 3 | 2 | 4 | 3 | 4 | 4 | 5 | 4 | 5 |
OÇ-5 | 2 | 3 | 4 | 2 | 4 | 3 | 5 | 4 | 5 | 4 | 5 |