Study Guide

Objectives (course unit)

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.

Content (course unit)

- 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

Prerequisites (course unit)

Basic knowledge of programming

Further information (course unit)

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

Assessment criteria, satisfactory (1-2) (course unit)

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.

Assessment criteria, good (3-4) (course unit)

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.

Assessment criteria, excellent (5) (course unit)

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.

Location and time

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

Exam schedules

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.

Assessment methods and criteria

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 learning diary and attendance on classes.

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

Assessment scale

0-5

Teaching methods

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

Learning materials

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

Student workload

75 hours contact teaching and 138 hours independent learning.

Content scheduling

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

Objectives (course unit)

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.

Content (course unit)

- 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

Prerequisites (course unit)

Basic knowledge of programming

Further information (course unit)

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

Assessment criteria, satisfactory (1-2) (course unit)

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.

Assessment criteria, good (3-4) (course unit)

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.

Assessment criteria, excellent (5) (course unit)

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.

Location and time

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

Exam schedules

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.

Assessment methods and criteria

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 learning diary and attendance on classes.

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

Assessment scale

0-5

Teaching methods

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

Learning materials

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

Student workload

75 hours contact teaching and 138 hours independent learning.

Content scheduling

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

Objectives (course unit)

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.

Content (course unit)

- 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

Prerequisites (course unit)

Basic knowledge of programming

Further information (course unit)

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

Assessment criteria, satisfactory (1-2) (course unit)

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.

Assessment criteria, good (3-4) (course unit)

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.

Assessment criteria, excellent (5) (course unit)

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.

Exam schedules

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.

Assessment methods and criteria

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

Assessment scale

0-5

Teaching methods

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

Learning materials

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

Student workload

75 hours contact teaching and 138 hours independent learning.

Content scheduling

Course content:

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