Tekoälyn perusteet Pythonilla (5 op)
Toteutuksen tunnus: TI00FA69-3009
Toteutuksen perustiedot
- Ilmoittautumisaika
-
02.12.2024 - 31.12.2024
Ilmoittautuminen toteutukselle on päättynyt.
- Ajoitus
-
01.01.2025 - 16.03.2025
Toteutus on päättynyt.
- Opintopistemäärä
- 5 op
- Toteutustapa
- Lähiopetus
- Yksikkö
- (2019-2024) ICT ja tuotantotalous
- Opetuskielet
- englanti
- Koulutus
- Tieto- ja viestintätekniikan tutkinto-ohjelma
Toteutuksella on 8 opetustapahtumaa joiden yhteenlaskettu kesto on 56 t 0 min.
Aika | Aihe | Tila |
---|---|---|
Ke 15.01.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMD758
Oppimistila
|
Ke 22.01.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMD758
Oppimistila
|
Ke 29.01.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMC565
Digitila
|
Ke 05.02.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMC565
Digitila
|
Ke 12.02.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMC565
Digitila
|
Ke 26.02.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMC565
Digitila
|
Ke 05.03.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMC565
Digitila
|
Ke 12.03.2025 klo 09:00 - 16:00 (7 t 0 min) |
Artificial Intelligence with Python TI00FA69-3009 |
KMC565
Digitila
|
Tavoitteet
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence using Python Programming Language in practise.
Sisältö
- 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
Arviointiasteikko
0-5
Arviointimenetelmät ja arvioinnin perusteet
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.