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Artificial Intelligence with Python (5 cr)

Code: TI00FA69-3008

General information


Enrollment
15.02.2025 - 16.03.2025
Registration for the implementation has ended.
Timing
17.03.2025 - 31.07.2025
Implementation is running.
Number of ECTS credits allocated
5 cr
Mode of delivery
On-campus
Unit
(2019-2024) School of ICT
Teaching languages
English
Degree programmes
Information and Communication Technology
Teachers
Kirpal Singh
Groups
LT6424S
Professional Development Program in Information Technology
Course
TI00FA69

Implementation has 15 reservations. Total duration of reservations is 45 h 0 min.

Time Topic Location
Tue 18.03.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 20.03.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Tue 25.03.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 27.03.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Tue 01.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 03.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Tue 08.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 10.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Tue 15.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 17.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Tue 22.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 24.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Tue 29.04.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Tue 06.05.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD758 Oppimistila
Thu 08.05.2025 time 13:00 - 16:00
(3 h 0 min)
Tekoälyn perusteet Pythonilla TI00FA69-3008
KMD759 Oppimistila
Changes to reservations may be possible.

Objective

After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence using Python Programming Language in practise.

Content

- Python Quick Recap
- Python Arrays, Tables, Vectors, Matrices
- AI: Short Description
- AI: Regression 1
- AI: Regression 2
- AI: Classification 1
- AI: Classification 2
- AI: Miscellanae

Evaluation scale

0-5

Assessment methods and criteria

Evaluation criteria - Satisfactory (1–2)
Basic understanding of AI concepts and Python tools:
• The student demonstrates basic understanding of AI concepts such as regression and classification.
• Can use Python to perform simple data manipulation (e.g., arrays, matrices).
• Can implement and explain basic regression or classification models using pre-existing templates.
• Requires guidance for model selection and evaluation.
________________________________________
Evaluation criteria - Good (3–4)

Independent application and explanation of core AI techniques:
• The student can implement regression and classification models using scikit-learn with appropriate preprocessing.
• Can evaluate model performance using standard metrics (e.g., accuracy, MSE).
• Can explain the differences between models and choose suitable ones for a given dataset.
• Shows some independent problem-solving and tuning of models.
________________________________________
Evaluation criteria - Excellent (5)

Advanced problem-solving, critical thinking, and elegant solutions:
• The student shows mastery in selecting and implementing appropriate AI models and techniques.
• Can clearly justify model choices and preprocessing steps based on data characteristics.
• Demonstrates ability to compare and improve models using metrics and visualizations.
• Provides well-structured, efficient, and readable code with critical reflection on model limitations and improvements.

________________________________________
Evaluation criteria - Approved

Student has achieved the course objectives fairly. Student will be able to identify, define and use the course subject area’s concepts and models. Student understands the criteria and principles of the expertise development.

Objective

After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence using Python Programming Language in practise.

Content

- Python Quick Recap
- Python Arrays, Tables, Vectors, Matrices
- AI: Short Description
- AI: Regression 1
- AI: Regression 2
- AI: Classification 1
- AI: Classification 2
- AI: Miscellanae

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