AI and Machine LearningLaajuus (8 cr)
Code: 5G00FT12
Credits
8 op
Objectives
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
- 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
Basic knowledge of programming
Assessment criteria, satisfactory (1-2)
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)
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)
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.
Further information
Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.
Enrolment period
09.06.2024 - 08.09.2024
Timing
26.08.2024 - 22.12.2024
Credits
8 op
Mode of delivery
Contact teaching
Unit
Software Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Juha Ranta-Ojala
- Miika Huikkola
Person in charge
Pekka Pöyry
Groups
-
22I260EA
-
22I260EBDegree Programme in Software Engineering
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 XX/2024. Second retake on week YY/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=44865
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
Enrolment period
15.07.2023 - 04.09.2023
Timing
28.08.2023 - 17.12.2023
Credits
8 op
Mode of delivery
Contact teaching
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Seats
0 - 45
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Esa Kujansuu
- Iina Nieminen
- Miika Huikkola
- Pekka Pöyry
Person in charge
Pekka Pöyry
Groups
-
21I260EA
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
Enrolment period
15.07.2023 - 04.09.2023
Timing
28.08.2023 - 17.12.2023
Credits
8 op
Mode of delivery
Contact teaching
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Esa Kujansuu
- Iina Nieminen
- Miika Huikkola
- Pekka Pöyry
Person in charge
Pekka Pöyry
Groups
-
21I260EB
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
Enrolment period
30.07.2022 - 11.09.2022
Timing
29.08.2022 - 23.12.2022
Credits
8 op
Mode of delivery
Contact teaching
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Seats
0 - 40
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Esa Kujansuu
- Iina Nieminen
- Miika Huikkola
- Pekka Pöyry
Person in charge
Pekka Pöyry
Groups
-
20I260E
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