Machine learning
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Machine learning is a field of study that allows computers to learn without being explicitly programmed by deriving patterns from large datasets. It involves using algorithms to analyze training data to generate a hypothesis for making predictions on test data, such as in natural language processing, data mining, and applications like IBM Watson. There are two main types of machine learning: supervised learning where correct answers are provided for learning, and unsupervised learning where algorithms must find structure in unlabeled data without guidance.
Machine learning
- 2. Preview - What is Machine Learning - Examples - IBM Watson - Supervised Learning - Unsupervised Learning - PredictionIO
- 3. Learning algorithms of large datasets
- 4. Field of study that gives computers the ability to learn without being explicitly programmed. - Arthur Samuel (1959)
- 5. Training Data Learning Algorithm Hypothesis (Mathematical model) Predicted y (feedback) Test Data
- 6. - Derives from AI theories - Machine Learning is NOT pattern matching! - Mathematical induction with certain confidence intervals in mind
- 7. Examples
- 9. I shot an elephant wearing my pajamas
- 11. Data Mining
- 14. 28:08 - 31:23
- 18. Supervised Learning - ‘right’ answers were given already
- 21. Unsupervised Learning - No test data - You need to find the structure yourself
- 24. Thanks!