The Elements of AILaajuus (2 cr)
Code: TT00DP30
Credits
2 op
Teaching language
- English
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Course is completed when you can done 80 % of the available exercises (20)
Materials
https://course.elementsofai.com/
Timing
06.01.2024 - 31.12.2023
Number of ECTS credits allocated
2 op
Virtual portion
2 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 2000
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Groups
-
ATX22_SYKSYATX22_Autumn
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Location and time
Riippuu opiskelijasta itsestään koska kyseessä on itseopiskeltava verkkokurssi.
Materials
Oppimateriaali: https://course.elementsofai.com/
Teaching methods
Itse opiskeltava verkkokurssi
Employer connections
N/A
Exam schedules
Tiedot löytyvät oppimisympäristöstä.
International connections
N/A
Completion alternatives
N/A
Student workload
Riippuu opiskelijasta itsestään.
Content scheduling
Riippuu opiskelijasta itsestään.
Further information
Avoimen amk:n sekä CampusOnlinen opiskelijat ilmoittautuvat kurssille e-lomakkeen kautta.
Metropolian tutkinto-opiskelijat:
1. Please log in to https://course.elementsofai.com, and sign-up.
2. Complete the course.
3. When you are finished, please forward your certification to Mrs. Prami (virve.prami@metropolia.fi) who will mark your course as passed.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Kun kurssista on suoritettu 80 % (20) harjoituksista suoritettu.
Assessment methods and criteria
Kurssista saa hyväksytty merkinnän kun siitä on suoritettu 80 % (20) harjoituksista.
Timing
01.01.2023 - 31.12.2023
Number of ECTS credits allocated
2 op
Virtual portion
2 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 2000
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Groups
-
ATX23TVNonStop virtual studies year 2023
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Location and time
Riippuu opiskelijasta itsestään koska kyseessä on itseopiskeltava verkkokurssi.
Materials
Oppimateriaali: https://course.elementsofai.com/
Teaching methods
Itse opiskeltava verkkokurssi
Employer connections
N/A
Exam schedules
Tiedot löytyvät oppimisympäristöstä.
International connections
N/A
Completion alternatives
N/A
Student workload
Riippuu opiskelijasta itsestään.
Content scheduling
Riippuu opiskelijasta itsestään.
Further information
Avoimen amk:n sekä CampusOnlinen opiskelijat ilmoittautuvat kurssille e-lomakkeen kautta.
Metropolian tutkinto-opiskelijat:
1. Please log in to https://course.elementsofai.com, and sign-up.
2. Complete the course.
3. When you are finished, please forward your certification to Mrs. Prami (virve.prami@metropolia.fi) who will mark your course as passed.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Kun kurssista on suoritettu 80 % (20) harjoituksista suoritettu.
Assessment methods and criteria
Kurssista saa hyväksytty merkinnän kun siitä on suoritettu 80 % (20) harjoituksista.
Timing
31.12.2022 - 31.12.2023
Number of ECTS credits allocated
2 op
Virtual portion
2 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 2000
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Groups
-
ATX22TVSOpen UAS: NonStop Autumn 2022
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Location and time
Riippuu opiskelijasta itsestään koska kyseessä on itseopiskeltava verkkokurssi.
Materials
Oppimateriaali: https://course.elementsofai.com/
Teaching methods
Itse opiskeltava verkkokurssi
Employer connections
N/A
Exam schedules
Tiedot löytyvät oppimisympäristöstä.
International connections
N/A
Completion alternatives
N/A
Student workload
Riippuu opiskelijasta itsestään.
Content scheduling
Riippuu opiskelijasta itsestään.
Further information
Avoimen amk:n sekä CampusOnlinen opiskelijat ilmoittautuvat kurssille e-lomakkeen kautta.
Metropolian tutkinto-opiskelijat:
1. Please log in to https://course.elementsofai.com, and sign-up.
2. Complete the course.
3. When you are finished, please forward your certification to Mrs. Prami (virve.prami@metropolia.fi) who will mark your course as passed.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Kun kurssista on suoritettu 80 % (20) harjoituksista suoritettu.
Assessment methods and criteria
Kurssista saa hyväksytty merkinnän kun siitä on suoritettu 80 % (20) harjoituksista.
Timing
01.01.2022 - 31.12.2022
Number of ECTS credits allocated
2 op
Virtual portion
2 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 2000
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Groups
-
ATX22TVNonStop virtual studies year 2022
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Location and time
Riippuu opiskelijasta itsestään koska kyseessä on itseopiskeltava verkkokurssi.
Materials
Oppimateriaali: https://course.elementsofai.com/
Teaching methods
Itse opiskeltava verkkokurssi
Employer connections
N/A
Exam schedules
Tiedot löytyvät oppimisympäristöstä.
International connections
N/A
Completion alternatives
N/A
Student workload
Riippuu opiskelijasta itsestään.
Content scheduling
Riippuu opiskelijasta itsestään.
Further information
Avoimen amk:n sekä CampusOnlinen opiskelijat ilmoittautuvat kurssille e-lomakkeen kautta.
Metropolian tutkinto-opiskelijat:
1. Please log in to https://course.elementsofai.com, and sign-up.
2. Complete the course.
3. When you are finished, please forward your certification to Mrs. Prami (virve.prami@metropolia.fi) who will mark your course as passed.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Kun kurssista on suoritettu 80 % (20) harjoituksista suoritettu.
Assessment methods and criteria
Kurssista saa hyväksytty merkinnän kun siitä on suoritettu 80 % (20) harjoituksista.
Timing
01.01.2022 - 31.12.2022
Number of ECTS credits allocated
2 op
Virtual portion
2 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 2000
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Groups
-
ATX22TVKOpe UAS: NonStop Spring 2022
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Location and time
Riippuu opiskelijasta itsestään koska kyseessä on itseopiskeltava verkkokurssi.
Materials
Oppimateriaali: https://course.elementsofai.com/
Teaching methods
Itse opiskeltava verkkokurssi
Employer connections
N/A
Exam schedules
Tiedot löytyvät oppimisympäristöstä.
International connections
N/A
Completion alternatives
N/A
Student workload
Riippuu opiskelijasta itsestään.
Content scheduling
Riippuu opiskelijasta itsestään.
Further information
Avoimen amk:n sekä CampusOnlinen opiskelijat ilmoittautuvat kurssille e-lomakkeen kautta.
Metropolian tutkinto-opiskelijat:
1. Please log in to https://course.elementsofai.com, and sign-up.
2. Complete the course.
3. When you are finished, please forward your certification to Mrs. Prami (virve.prami@metropolia.fi) who will mark your course as passed.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Kun kurssista on suoritettu 80 % (20) harjoituksista suoritettu.
Assessment methods and criteria
Kurssista saa hyväksytty merkinnän kun siitä on suoritettu 80 % (20) harjoituksista.
Timing
15.03.2021 - 31.12.2023
Number of ECTS credits allocated
2 op
Virtual portion
2 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 500
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
CareerBoost_TXK_21Career Boost 21 (Tivi)
-
CareerBoost_TXK_22Career Boost 22 (TiVi)
Objective
After completing the course, the student will be able to:
- Identify autonomy and adaptivity as key concepts of AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
- Formulate a real-world problem as a search problem
- Formulate a simple game (such as tic-tac-toe) as a game tree
- Use the minimax principle to find optimal moves in a limited-size game tree
- Express probabilities in terms of natural frequencies
- Apply the Bayes rule to infer risks in simple scenarios
- Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
- Explain why machine learning techniques are used
- Distinguish between unsupervised and supervised machine learning scenarios
- Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
- Explain what a neural network is and where they are being successfully used
- Understand the technical methods that underpin neural networks
- Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
- Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work “
Content
1. What is AI?
a. motivaation
b. definition of AI
c. philosophy of AI
2. AI problem solving
a. formulating and solving problems using state diagrams
b. formulating simple games (tic-tac-toe or chess) as game trees
c. solving game trees using the minimax algorithm
3. Real world AI
a. expressing uncertainty using probability
b. probabilities and odds
c. Bayes formula
4. Machine learning
a. nearest neighbor classifier
b. linear regression
c. logistic regression
5. Neural networks
a. concepts of neural computation
b. learning in neural networks
c. perceptron classifier
6. Implications
a. public perception of AI
b. critical evaluation of claims made about AI (e.g., singularity, AI winter)
c. societal implications and ethics of AI
Location and time
Riippuu opiskelijasta itsestään koska kyseessä on itseopiskeltava verkkokurssi.
Materials
Oppimateriaali: https://course.elementsofai.com/
Teaching methods
Itse opiskeltava verkkokurssi
Employer connections
N/A
Exam schedules
Tiedot löytyvät oppimisympäristöstä.
International connections
N/A
Completion alternatives
N/A
Student workload
Riippuu opiskelijasta itsestään.
Content scheduling
Riippuu opiskelijasta itsestään.
Further information
Avoimen amk:n sekä CampusOnlinen opiskelijat ilmoittautuvat kurssille e-lomakkeen kautta.
Metropolian tutkinto-opiskelijat:
1. Please log in to https://course.elementsofai.com, and sign-up.
2. Complete the course.
3. When you are finished, please forward your certification to Mrs. Prami (virve.prami@metropolia.fi) who will mark your course as passed.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
.
Assessment criteria, approved/failed
Kun kurssista on suoritettu 80 % (20) harjoituksista suoritettu.
Assessment methods and criteria
Kurssista saa hyväksytty merkinnän kun siitä on suoritettu 80 % (20) harjoituksista.