Artificial Intelligence with Python (5 ECTS)
Code: TI00FA69-3014
General information
- Timing
- 01.08.2025 - 19.10.2025
- The implementation has not yet started.
- Number of ECTS credits allocated
- 5 ECTS
- Mode of delivery
- On-campus
- Unit
- School of ICT and Industrial Management
- Campus
- Karaportti 2
- Teaching languages
- English
- Degree programmes
- Further Education Programme in Technology, Communication and Transport
- Teachers
- Kirpal Singh
- Groups
-
LT6425K01Professional Development Program in Information Technology
- Course
- TI00FA69
Implementation has 8 reservations. Total duration of reservations is 51 h 30 min.
Time | Topic | Location |
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Thu 21.08.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
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Thu 28.08.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
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Thu 04.09.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
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Thu 11.09.2025 time 09:00 - 11:30 (2 h 30 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
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Thu 18.09.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
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Thu 25.09.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
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Thu 02.10.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
Oppimistila
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Thu 09.10.2025 time 09:00 - 16:00 (7 h 0 min) |
Artificial Intelligence with Python TI00FA69-3014 |
KMD759
Oppimistila
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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.
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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.
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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.
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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.