26
Large Language Models
ADA511
0.3
2025-08-22
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
5
What is an inference?
6
Sentences
7
Truth inference
8
Probability inference
9
Shortcut rules
10
Monty Hall and related inference problems
11
Second connection with machine learning
Data I
12
Quantities and data types
13
Joint quantities and complex data types
Inference II
14
Probability distributions
15
Joint probability distributions
16
Marginal probability distributions
17
Conditional probability and learning
Learning and conditional probability: a summary
18
Information, relevance, independence, association
19
Third connection with machine learning
Data II
20
Populations and variates
21
Statistics
22
Subpopulations and conditional frequencies
23
Infinite populations and samples
Machine learning
24
Introduction to machine learning
25
Neural networks
26
Large Language Models
Inference III
27
Beyond machine learning
28
Exchangeable beliefs
29
Inferences from frequencies
30
Inference about frequencies
31
Final inference formulae
A prototype Optimal Predictor Machine
32
The Dirichlet-mixture belief distribution
33
Code design
34
Prototype code and workflow
35
Example application: adult-income task
Decision-making
36
Utilities
37
Making decisions
38
The prototype Optimal Predictor Machine makes decisions
Further connections with present-day machine-learning
39
Decisions: limitations of present-day machine-learning algorithms
40
Evaluation practices and utilities
Conclusion
What next?
Further reading
Thanks
References
26
Large Language Models
Published
2025-08-22
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To be written
25
Neural networks
27
Beyond machine learning