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Understanding Artificial Intelligence through Algorithmic Information Theory
Institut Mines-Tlcom via edX
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Overview
Artificial Intelligence is more than just a collection of brilliant, innovative methods to solve problems. If you are interested in machine learning or are planning to explore it, the course will make you see artificial learning in an entirely new way. You will know how to formulate optimal hypotheses for a learning task. And you will be able to analyze learning techniques such as clustering or neural networks as just ways of compressing information. If you are interested in...
Syllabus
Chapter 1. Describing data
- Complexity as code length
- Conditional Complexity
Chapter 2. Measuring Information
- Complexity and frequency
- Meaning distance
Chapter 3. Algorithmic information & mathematics
- Algorithmic probability, Randomness
- Gdels theorem
Chapter 4. Machine Learning and Algorithmic Information
- Universal induction - MDL
- Analogy & Machine Learning as complexity minimization
Chapter 5. Subjective information
- Simplicity & coincidences
- Subjective probability
- Relevance
Instructor
Jean-Louis Dessalles
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