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The Optimal Predictor Machine generates text
ADA511
0.4
2025-11-03
licence
Dear student
and aspiring data- & AI-engineer
Preface
An invitation
1
Accept or discard?
2
Framework
3
Basic decision problems
4
Connection with machine learning and AI
Inference I
Working with R, I
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What is an inference?
6
Sentences
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Truth inference
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Probability inference
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Shortcut rules
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Monty Hall and related inference problems
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Second connection with machine learning
Data I
12
Quantities and data types
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Joint quantities and complex data types
Inference II
14
Probability distributions
Working with R, II
15
Joint probability distributions
Working with R, III
16
Marginal probability distributions
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Conditional probability and learning
Learning and conditional probability: a summary
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Information, relevance, independence, association
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Third connection with machine learning
Data II
20
Populations and variates
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Statistics
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Subpopulations and conditional frequencies
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Infinite populations and samples
Inference III
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A categorization of inferences
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Exchangeable beliefs
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Inferences from frequencies
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Inference about frequencies
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Example of belief over frequencies: the Dirichlet-mixture distribution
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Final inference formulae for exchangeable beliefs
A prototype Optimal Predictor Machine
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A look behind
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Implementing an OPM
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Prototype code and workflow
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Example application: adult-income task
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Example application: “small language model”
Decision-making
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Utilities
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Making decisions
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The prototype Optimal Predictor Machine makes decisions
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The Optimal Predictor Machine generates text
Approximations: machine learning
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Introduction to machine learning
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Decision trees
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Neural networks
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Decisions: limitations of present-day machine-learning algorithms
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Evaluation practices and utilities
Conclusion
What next?
Further reading
Thanks
References
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The Optimal Predictor Machine generates text
Published
2025-11-03
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To be written.
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The prototype Optimal Predictor Machine makes decisions
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Introduction to machine learning