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Tekoälyn perusteet Pythonilla (5 op)

Toteutuksen tunnus: TI00FA69-3014

Toteutuksen perustiedot


Ajoitus
01.08.2025 - 19.10.2025
Toteutus ei ole vielä alkanut.
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
ICT ja tuotantotalous
Toimipiste
Karaportti 2
Opetuskielet
englanti
Koulutus
Tekniikan ja liikenteen alan täydennyskoulutus
Opettajat
Kirpal Singh
Ryhmät
LT6425K01
Professional Development Program in Information Technology
Opintojakso
TI00FA69

Toteutuksella on 8 opetustapahtumaa joiden yhteenlaskettu kesto on 51 t 30 min.

Aika Aihe Tila
To 21.08.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 28.08.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 04.09.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 11.09.2025 klo 09:00 - 11:30
(2 t 30 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 18.09.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 25.09.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 02.10.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
To 09.10.2025 klo 09:00 - 16:00
(7 t 0 min)
Artificial Intelligence with Python TI00FA69-3014
KMD759 Oppimistila
Muutokset varauksiin voivat olla mahdollisia.

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.
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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.
________________________________________
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.

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