Curricula

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

The course will be implemented in the fall semester of 2024.

Learning materials and recommended literature

The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.12+ environment, Git version control, GitLab repositories, scikit-learn, Pandas, visualization libraries and other applicable libraries.

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.

Practical training and working life connections

The aim is to connect the content of the course to problems that occur in working life.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.

Further information for students

The course is evaluated based on the returned assignments.
The assessment methods are reviewed at the beginning of the course.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

The course will be implemented in the spring semester of 2024.

Learning materials and recommended literature

The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.9+ environment, Git version control, scikit-learn, Pandas, visualization libraries and other applicable libraries.

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.

Practical training and working life connections

The aim is to connect the content of the course to problems that occur in working life.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.

Further information for students

The course is evaluated based on the returned assignments.
The assessment methods are reviewed at the beginning of the course.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

The course will be implemented in the fall semester of 2023.

Learning materials and recommended literature

The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.9+ environment, Git version control, scikit-learn, Pandas, visualization libraries and other applicable libraries.

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.

Practical training and working life connections

The aim is to connect the content of the course to problems that occur in working life.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.

Further information for students

The course is evaluated based on the returned assignments.
The assessment methods are reviewed at the beginning of the course.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

Opintojakso toteutetaan kevätlukukaudella 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, kirjastoista scikit-learn ja Pandas, visualisointikirjastoja sekä muita soveltuvia kirjastoja.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

Opintojakso toteutetaan kevätlukukaudella 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, scikit-learn, Pandas, visualisointikirjastoja sekä muita soveltuvia kirjastoja.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

Opintojakso toteutetaan syyslukukaudella 2022.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, scikit-learn, Pandas, visualisointikirjastoja sekä muita soveltuvia kirjastoja.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

Opintojakso toteutetaan syyslukukaudella 2022.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, scikit-learn, Pandas, visualisointikirjastoja sekä muita soveltuvia kirjastoja.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .

Objectives

You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Supervised and unsupervised machine learning and the most common regression and classification models
- Application using Python libraries (NumPy, Pandas and scikit-learn)
- Data format and quality
- Splitting of the data set into training and test data
- Evaluation of model accuracy

Different models of machine learning:
- k-nearest neighbors
- k-means clustering
- Naive Bayes method
- Support Vector Machine
- Principal Component Analysis (PCA)
- Decision trees and random forest
- Perceptron (simple neural network)

Time and location

Opintojakso toteutetaan alkuvuodesta 2022.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Kurssilla hyödynnetään Python 3.7+-ympäristöä, git-versiohallintaa, scikit-learn-kirjastoa ja muita koneoppimiskirjastoja. Lisäksi hyvän pohjan antavat esimerkiksi seuraavat:

[1] Simeone O. (2018). A Brief Introduction to Machine Learning for Engineers. arXiv preprint arXiv:1709.02840v3 [cs.LG]. (237 pages) https://arxiv.org/abs/1709.02840

[2] Hastie, T., Tibshirani R., & Friedman J. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. (764 pages) https://web.stanford.edu/~hastie/ElemStatLearn/

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyvään sisältöön perehtymisen.

Practical training and working life connections

Kurssin sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Kurssi arvioidaan palautettujen harjoitustehtävien avulla.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Further information for students

Arvosana määräytyy alla olevien osaamistasojen mukaisesti:

Erinomainen 5: Opiskelija tunnistaa koneoppimisen tuomat hyödyt digitalisaation aikakautena. Opiskelija osaa koneoppimisen yleisimmin käytetyt tekniikat ja osaa kriittisesti perustella käytettyjen tekniikoiden käytön erilaisissa koneoppimistehtävissä. Hän osaa kriittisesti perustella ja valita oikeat tekniikat koneoppimiseen riippumatta lähdeaineistosta ja osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida kriittisesti toteutuksensa ja perustella sen kehittämistä.

Kiitettevä 4: Opiskelija tunnistaa koneoppimisen tuomat hyödyt digitalisaation aikakautena. Opiskelija osaa koneoppimisen yleisimmin käytetyt tekniikat ja osaa laajasti perustella käytettyjen tekniikoiden käytön erilaisissa koneoppimistehtävissä. Hän osaa monipuolisesti perustella ja valita oikeat tekniikat koneoppimiseen ja hän osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida perusteellisesti toteutuksensa ja perustella sen kehittämistä.

Hyvä 3: Opiskelija tiedostaa koneoppimisenn hyödyt digitalisaation aikakautena. Opiskelija tietää koneoppimisen yleisimmin käytetyt tekniikat erilaisissa koneoppimistehtävissä. Hän osaa perustella ja valita tekniikat koneoppimiseen ja hän osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida toteutuksensa ja perustella sen kehittämistä.

Tyydyttävä 2: Opiskelija tietää koneoppimisen yleisimmin käytetyt tekniikat koneoppimistehtävissä. Hän osaa valita tekniikat koneoppimiseen ja hän osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida pintapuolisesti toteutuksensa.

Välttävä 1: Opiskelija tietää koneoppimisen yleisimmin käytetyt tekniikat koneoppimistehtävissä. Hän osaa soveltaa yleisimpiä tekniikoita koneoppimisessa. Lisäksi opiskelija osaa arvioida suppeasti toteutuksensa.

Hylätty 0: Opiskelija ei hallitse aihealuetta.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

Prerequisites

Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
Additionally, courses in Computational algorithms and Data Preprocessing .