Study Guide

Objective

After completing the course, the student can:
- describe what kind of AI applications are available
- describe how AI based applications can be developed
- develop applications using AI

Content

Actual content is decided during the course implementation phase.
The contents vary every year.

Materials

Material available via the learning environment (ITS).

Teaching methods

The course includes about 12 theory sessions and personal practice tasks (3h),

There will be also quest lecturers (from companies or RDI people)

Exam schedules

No exam or in week 17.

International connections

This learning method combines theoretical knowledge with practical applications and real-world examples.
Weekly assignments based on the topics covered.
Around half of the exercises are done during the contact hours.
Additionally, exercises for home work.

Additionally:
- Mid-term project: Develop a simple AI application
- Final project/exam: Comprehensive AI application using multiple techniques learned in the course

Completion alternatives

None.

Student workload

Contact hours:
- Week 3: Course Introduction 2h
- Weeks 4 - 16: Theory & practice (3h/week): 12 x 3h = 36h
- Week 17: Exam/Finals 2h
Total contact hours: 40 hours

Independent study and homework: about 90 h

Total: approximately: 130 hours

Content scheduling

Weekly time schedule plan
3. Introduction to Course and AI-based applications & Examples of AI-Based Applications in various industries
4. Steps to develop AI Applications with a help of tools and frameworks
5. Data-Driven AI and techniques for data-driven AI development
6. Use of Open Data and building Decision Trees with it
7. Handling and processing tabular data and applications of tabular data
9. Generative AI and applications of generative AI (e.g., art, music, text generation)
10. Language Models (e.g., GPT, BERT) and NLP applications NLP
11. Computer Vison and it's real-world applications (e.g., facial recognition, autonomous vehicles)
12. Object Recognition and techniques & applications for object recognition
13. Synthetic Data and use cases of it.
14. Optimization in AI and applications of optimization in AI models
15. IBM Watson and practical applications with it (e.g. image recognition, NLP)
16. Building and training models using PyTorch & TensorFlow
17. Exam/Presentation of final project results
+ projects to build an AI application during the course (one alone and another in team)

Further information

ItsLearning

Evaluation scale

H-5

Assessment methods and criteria

You can achieve points from participation, exercises, participation and exam/final project:
- 20% points from participation
- 50% points from practical exercises in class room and home work
- 30% points from the final project work/exam