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Applications of AI (5 cr)

Code: TT00CN77-3001

General information


Enrollment
04.12.2024 - 16.01.2025
Registration for the implementation has ended.
Timing
16.01.2025 - 30.04.2025
Implementation has ended.
Number of ECTS credits allocated
5 cr
Local portion
5 cr
RDI portion
2 cr
Mode of delivery
Contact learning
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
English
Seats
0 - 40
Degree programmes
Degree Programme in Business Information Technology
Degree Programme in Information and Communication Technology
Teachers
Golnaz Sahebi
Pertti Ranttila
Ali Khan
Jussi Salmi
Groups
PTIETS22deai
PTIETS22 Data Engineering and Artificial Intelligence
PTIVIS22I
Data Engineering and AI
Course
TT00CN77

Realization has 14 reservations. Total duration of reservations is 38 h 30 min.

Time Topic Location
Thu 16.01.2025 time 09:00 - 10:00
(1 h 0 min)
Course Introduction, Applications of AI TT00CN77-3001
ICT_C1042_Myy MYY
Tue 21.01.2025 time 12:00 - 15:00
(3 h 0 min)
AWS NLP Self Paced, Applications of AI TT00CN77-3001
ICT_C3036 Cyberlab / BYOD
Mon 27.01.2025 time 09:00 - 12:00
(3 h 0 min)
AWS NLP Self Paced, Applications of AI TT00CN77-3001
ICT_C3039 Ciscolaboratorio
Thu 06.02.2025 time 09:00 - 12:00
(3 h 0 min)
AWS NLP Self Paced, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 13.02.2025 time 09:00 - 12:00
(3 h 0 min)
AWS NLP Self Paced, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 27.02.2025 time 08:00 - 11:00
(3 h 0 min)
AWS NLP Self Paced, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 06.03.2025 time 08:00 - 11:00
(3 h 0 min)
Theory & Practices, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 13.03.2025 time 08:00 - 11:00
(3 h 0 min)
Theory & Practices, Applications of AI TT00CN77-3001
LEM_A176 IT-tila Micrococcus - koneilla
Thu 20.03.2025 time 08:00 - 11:00
(3 h 0 min)
Theory & Practices, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 27.03.2025 time 08:00 - 11:00
(3 h 0 min)
Theory & Practices, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 03.04.2025 time 08:00 - 11:00
(3 h 0 min)
Theory & Practices, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 10.04.2025 time 09:30 - 12:00
(2 h 30 min)
NLP Final Project Presentations, Applications of AI TT00CN77-3001
ICT_C1039_Sigma SIGMA
Thu 17.04.2025 time 08:00 - 11:00
(3 h 0 min)
Theory & Practices, Applications of AI TT00CN77-3001
ICT_C2027 IT-tila - telakka
Thu 24.04.2025 time 08:00 - 10:00
(2 h 0 min)
Finals or exam, Applications of AI TT00CN77-3001
ICT_C1032 Demotila
Changes to reservations may be possible.

Evaluation scale

H-5

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

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.

Pedagogic approaches and sustainable development

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
Self paced AWS academy Module (3h/week): 6 x 3h = 18h
- Weeks 4 - 9 & Week 15: NLP - Total 6 weeks
- Week 8 - Winter Holidays
- Week 15 Final Project Presentations NLP
Theory & practice (3h/week): 6 x 3h = 18h
- Weeks 10 - 14 & 16 : Image Applications - Total 6 weeks
- Week 17: Exam/Finals 2h
Total contact hours: 40 hours

Independent study and homework: about 90 h

Total: approximately: 130 hours

Evaluation 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.

Failed (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

Further information

ItsLearning

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