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2019 Helsinki University of Technology Course Certificate Course included: 01. Identify autonomy and adaptivity as key concepts of AI 02. Distinguish between realistic and unrealistic AI (science fiction vs. real life) 03. Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment 04. Formulate a real-world problem as a search problem 05. Formulate a simple game (such as tic-tac-toe) as a game tree 06. Use the minimax principle to find optimal moves in a limited-size game tree 07. Express probabilities in terms of natural frequencies 08. Apply the Bayes rule to infer risks in simple scenarios 09. Explain the base-rate fallacy and avoid it by applying Bayesian reasoning 10. Explain why machine learning techniques are used 11. Distinguish between unsupervised and supervised machine learning scenarios 12. Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression 13. Explain what a neural network is and where they are being successfully used 14. Understand the technical methods that underpin neural networks 15. Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI 16. Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work |
| 🌑About |
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| 🔴Skills & Qualifications |
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⭘2020-present  |
🔴2013-2020  |
⭘1997-2013  |
⭘1990-1997  |
⭘1978-1990  |