The Elements of AILaajuus (2 cr)

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)

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Assessment criteria, approved/failed

Course is completed when you can done 80 % of the available exercises (20)

Materials

https://course.elementsofai.com/