Project Work: Artificial Intelligence (AI) (4 cr)

Code: TTOW1500-3004

General information

Timing: 01.11.2021 - 31.12.2021
Implementation has ended.

Number of ECTS credits allocated: 4 cr

Local portion: 4 cr

Mode of delivery: Face-to-face

Unit: School of Technology

Campus: Lutakko Campus

Teaching languages: Finnish

Teachers: Janne Alatalo; Eppu Heilimo

Groups: ZJA21KTIDA
Avoin AMK, tekniikka, ICT, Data-analytiikka

Course: TTOW1500

No reservations found for realization TTOW1500-3004!

Evaluation scale

0-5

Objective

The student understands and masters the various phases of Articial Intelligence project. The student is able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. The student is able to interpret the obtained results and draw conclusions based on them.

Content

• AI assignment based on optional (elective) data
• Data preprocessing
• Choice of neural network
• Building a neural network
• Choice of training set and test set
• Assessment of results
• Detailed reporting (GitLab)

Assessment criteria, satisfactory (1)

Excellent 5: The student knows the various phases of an AI project and is able to systematically proceed step by step.
The student is able to select the correct techniques regardless of the problem to be solved and apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation critically and validate the conclusions.

Very good4: The student knows the various phases of an AI project and is able to proceed systematically step by step. The student is able to select the correct techniques regardless of the problem to be solved and apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation versatilely and validate the conclusions.

Good 3: The student knows the various phases of an AI project and is able to proceed step by step. The student is able to select the most common techniques to the problem to be solved and is able to apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation versatilely and validate the conclusions.

Satisfactory 2: The student knows the most common phases of an AI project. The student is able to select the most common techniques to the problem to be solved and is able to apply his/her technical know-how to practice. Additionally, the student is able to assess his/her implementation superficially and validate the conclusions.

Sufficient 1: The student knows the various phases of an AI project. The student knows the most common techniques and is able to apply them to practice. Additionally, the student is able to assess his/her implementation and conclusions briefly.

Fail 0: The student does not meet the minimum criteria set for the course.

Qualifications

Basics in computing, programming, knowledge and know-how of Python programming language.

Additionally, courses in Data Preprocessing, Machine Learning and Deep Learning.