Opinto-opas

Tavoitteet (OJ)

The student understands basic concepts of AI and Machine Learning. The student is able to create and use Machine Learning Algorithms in Python. The student learns how to make analysis and predictions and knows which Machine Learning model to choose for each type of a problem.

Sisältö (OJ)

- Basic concepts of AI and Machine Learning
- Unsupervised and Supervised learning
- Regression, Association, Classification
- Naïve Bayes, Decision Trees and Neural Network Algorithms
- Training and validation of models
- Production testing of models

Esitietovaatimukset (OJ)

Basic knowledge of programming

Lisätiedot (OJ)

Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.

Arviointikriteerit, tyydyttävä (1-2) (OJ)

Student knows about the basic concepts of AI and Machine Learning. Student can apply at least some supervised or supervised learning applications. Student can use regression, association or classification algorithm with support. Student can create an application using either Naïve Bayes, Decision Trees or Neural Network Algorithms. Student can setup training and validation processes for new models. Student can setup production testing for new models.

Arviointikriteerit, hyvä (3-4) (OJ)

Student knows and understands the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning applications. Student can create applications with regression, association, or classification algorithms. Student can create working applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can setup and apply training and use validation methods for new models. Student can follow procedures of production testing for new models.

Arviointikriteerit, kiitettävä (5) (OJ)

Student knows and understands in depth the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning for various applications. Student can use regression, association, and classification algorithms where appropriate. Student can create versatile applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can implement various training and validation solutions for new models. Student is able to execute reliable production testing for new models.

Aika ja paikka

AI & ML: 3 hours per week in classroom
Mathematics: 1 hour online, 2 hours in classroom per week (5 weeks total)

Tenttien ja uusintatenttien ajankohdat

No exam.

Retake and improvement of the grade :
First retake on week 5/2024. Second retake on week 10/2024. A student contacts the lecturer during the retake week for detailed instructions. Improvement of the grade can be tried once during the retake weeks.

Arviointimenetelmät ja arvioinnin perusteet

The course consists of two separate parts: ML&AI and Mathematics. A student gets a separate grade from both parts. The final course grade is weighted average of the grades of the parts. ML&AI is 5/8 of the final course grade and Mathematics is 3/8 of the final course grade.

ML&AI:
A student can get points from two separate final practical works. Max. points for Practical work 1 is 20 points. Max. points for Practical work 2 is 30 points.

ML&AI points and grades:
0 0
10 1
17 2
25 3
35 4
45 5

--------------

Mathematics:
The scores in Mathematics part are received from attendance (20%) and activity on classes (30%) and assigments (50%).

Mathematics part grade thresholds are:
0 0
10 1
17 2
25 3
35 4
45 5

Arviointiasteikko

0-5

Opiskelumuodot ja opetusmenetelmät

AI & ML: 3 hours per week in classroom
Mathematics: 1 hour online, 2 hours in classroom per week (5 weeks total)

Oppimateriaalit

Course materials in Moodle:
https://moodle.tuni.fi/course/view.php?id=36931

Opiskelijan ajankäyttö ja kuormitus

75 hours contact teaching and 138 hours independent learning.

Sisällön jaksotus

Course schedule is in course Moodle.

Course content:

Basics of Machine Learning and AI
Linear Regression
Logistic Regression
Decision Tree
Random Forest
ANN
CNN
Mathematics

Tavoitteet (OJ)

The student understands basic concepts of AI and Machine Learning. The student is able to create and use Machine Learning Algorithms in Python. The student learns how to make analysis and predictions and knows which Machine Learning model to choose for each type of a problem.

Sisältö (OJ)

- Basic concepts of AI and Machine Learning
- Unsupervised and Supervised learning
- Regression, Association, Classification
- Naïve Bayes, Decision Trees and Neural Network Algorithms
- Training and validation of models
- Production testing of models

Esitietovaatimukset (OJ)

Basic knowledge of programming

Lisätiedot (OJ)

Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.

Arviointikriteerit, tyydyttävä (1-2) (OJ)

Student knows about the basic concepts of AI and Machine Learning. Student can apply at least some supervised or supervised learning applications. Student can use regression, association or classification algorithm with support. Student can create an application using either Naïve Bayes, Decision Trees or Neural Network Algorithms. Student can setup training and validation processes for new models. Student can setup production testing for new models.

Arviointikriteerit, hyvä (3-4) (OJ)

Student knows and understands the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning applications. Student can create applications with regression, association, or classification algorithms. Student can create working applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can setup and apply training and use validation methods for new models. Student can follow procedures of production testing for new models.

Arviointikriteerit, kiitettävä (5) (OJ)

Student knows and understands in depth the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning for various applications. Student can use regression, association, and classification algorithms where appropriate. Student can create versatile applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can implement various training and validation solutions for new models. Student is able to execute reliable production testing for new models.

Aika ja paikka

AI & ML: 3 hours per week in classroom
Mathematics: 1 hour online, 2 hours in classroom per week (5 weeks total)

Tenttien ja uusintatenttien ajankohdat

No exam.

Retake and improvement of the grade :
First retake on week 5/2024. Second retake on week 10/2024. A student contacts the lecturer during the retake week for detailed instructions. Improvement of the grade can be tried once during the retake weeks.

Arviointimenetelmät ja arvioinnin perusteet

The course consists of two separate parts: ML&AI and Mathematics. A student gets a separate grade from both parts. The final course grade is weighted average of the grades of the parts. ML&AI is 5/8 of the final course grade and Mathematics is 3/8 of the final course grade.

ML&AI:
A student can get points from two separate final practical works. Max. points for Practical work 1 is 20 points. Max. points for Practical work 2 is 30 points.

ML&AI points and grades:
0 0
10 1
17 2
25 3
35 4
45 5

--------------

Mathematics:
The scores in Mathematics part are received from attendance (20%) and activity on classes (30%) and assigments (50%).

Mathematics part grade thresholds are:
0 0
10 1
17 2
25 3
35 4
45 5

Arviointiasteikko

0-5

Opiskelumuodot ja opetusmenetelmät

AI & ML: 3 hours per week in classroom
Mathematics: 1 hour online, 2 hours in classroom per week (5 weeks total)

Oppimateriaalit

Course materials in Moodle:
https://moodle.tuni.fi/course/view.php?id=36932

Opiskelijan ajankäyttö ja kuormitus

75 hours contact teaching and 138 hours independent learning.

Sisällön jaksotus

Course schedule is in course Moodle.

Course content:

Basics of Machine Learning and AI
Linear Regression
Logistic Regression
Decision Tree
Random Forest
ANN
CNN
Mathematics

Tavoitteet (OJ)

The student understands basic concepts of AI and Machine Learning. The student is able to create and use Machine Learning Algorithms in Python. The student learns how to make analysis and predictions and knows which Machine Learning model to choose for each type of a problem.

Sisältö (OJ)

- Basic concepts of AI and Machine Learning
- Unsupervised and Supervised learning
- Regression, Association, Classification
- Naïve Bayes, Decision Trees and Neural Network Algorithms
- Training and validation of models
- Production testing of models

Esitietovaatimukset (OJ)

Basic knowledge of programming

Lisätiedot (OJ)

Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.

Arviointikriteerit, tyydyttävä (1-2) (OJ)

Student knows about the basic concepts of AI and Machine Learning. Student can apply at least some supervised or supervised learning applications. Student can use regression, association or classification algorithm with support. Student can create an application using either Naïve Bayes, Decision Trees or Neural Network Algorithms. Student can setup training and validation processes for new models. Student can setup production testing for new models.

Arviointikriteerit, hyvä (3-4) (OJ)

Student knows and understands the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning applications. Student can create applications with regression, association, or classification algorithms. Student can create working applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can setup and apply training and use validation methods for new models. Student can follow procedures of production testing for new models.

Arviointikriteerit, kiitettävä (5) (OJ)

Student knows and understands in depth the basic concepts of AI and Machine Learning. Student can apply both supervised and supervised learning for various applications. Student can use regression, association, and classification algorithms where appropriate. Student can create versatile applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. Student can implement various training and validation solutions for new models. Student is able to execute reliable production testing for new models.

Tenttien ja uusintatenttien ajankohdat

No exam.

Retake and improvement of the grade :
First retake on week 5/2023. Second retake on week 10/2023. A student contacts the lecturer during the retake week for detailed instructions. Improvement of the grade can be tried once during the retake weeks.

Arviointimenetelmät ja arvioinnin perusteet

The course consists of two separate parts: ML&AI and Mathematics. A student gets a separate grade from both parts. The final course grade is weighted average of the grades of the parts. ML&AI is 2/3 of the final course grade and Mathematics is 1/3 of the final course grade.

ML&AI:
A student can get points both from week projects (max. 30 points) and from a final practical work (max. 30 points).
ML&AI points and grades:
0 0
15 1
25 2
33 3
40 4
50 5

Mathematics:
The scores in Mathematics part are received from learning diary and attendance on classes.

Mathematics:
0 0
14 1
18 2
22 3
26 4
30 5

Arviointiasteikko

0-5

Opiskelumuodot ja opetusmenetelmät

3 hours per week in classroom and 2 hours per week in Teams.

Oppimateriaalit

Course materials in Moodle:
https://moodle.tuni.fi/course/view.php?id=29327

Opiskelijan ajankäyttö ja kuormitus

75 hours contact teaching and 138 hours independent learning.

Sisällön jaksotus

Course content:

Basics of Machine Learning and AI
Linear Regression
Logistic Regression
Clustering
Decision Tree & Random Forest
ANN
CNN
Mathematics