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 guest 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 (everyone have own project) for example in AWS academy NLP
- Final project/exam: Comprehensive AI application using multiple techniques learned in the course (group work)

Completion alternatives

None.

Student workload

Contact hours:
- Week 3: Course Introduction 2h
Theory & practice (3h/week): 12 x 3h = 36h
- Weeks 4 - 8 & 15: NLP - Total 6 weeks
- Weeks 9 - 14: Image Applications - Total 6 weeks
- Week 16: Exam/Finals 2h
Total contact hours: 40 hours

Independent study and homework: about 90 h

Total: approximately: 130 hours

Content scheduling

Part 1 NLP that covers 50% of the course is based on the AWS academy online course for NLP Natural Language Processing that includes the following modules:
Module 1 - Welcome to AWS Academy NLP
Module 2 - Introduction to Natural Language Processing (NLP)
Module 3 - Processing Text for NLP
Module 4 - Implementing Sentiment Analysis
Module 5 - Introducing Information Extraction
Module 6 - Introducing Topic Modeling
Module 7 - Working with Languages
Module 8 - Working with Generative AI
Module 9 - Course Wrap-up

Overall Topics:
1. Introduction to Course and AI-based applications & Examples of AI-Based Applications in various industries, AWS Academy registration
2. Steps to develop AI Applications with a help of tools and frameworks
3. Generative AI and applications of generative AI (e.g., art, music, text generation)
4. Language Models (e.g., GPT, BERT) and NLP applications NLP
5. Computer Vison and it's real-world applications (e.g., facial recognition, autonomous vehicles)
6. Object Recognition and techniques & applications for object recognition
+ projects to build an AI application during the course

Further information

ItsLearning

Evaluation scale

H-5

Assessment methods and criteria

For NLP Part:
AWS Academy Course labs: 40 points
Project: 10 points

For Image Applications Part:
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

It is mandatory to get at least 50% points in each of the above parts (NLP and Image Applications) to pass this course.

Assessment criteria, fail (0)

Under 50

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5