Lecture 3 Probability Theory

•

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Statistical methods and Natural Language Processing/Language Technology Notion of Probability Sample Spaces Events Axioms of Probability Theorems of Probability Conditional Probability Independence and Incompatibility

Educación

Machine
Learning
for
Language
Technology

Lecture
3:
Probability
Theory

Marina
San6ni

Department
of
Linguis6cs
and
Philology

Uppsala
University,
Uppsala,
Sweden

Autumn
2014

Acknowledgement:
Thanks
to
Prof.
Joakim
Nivre
for
course
design
and
materials

Outline

•  Sta6s6cal
methods
and
Natural
Language
Processing/
Language
Technology

•  No6on
of
Probability

•  Sample
Spaces

•  Events

•  Axioms
of
Probability

•  Theorems
of
Probability

•  Condi6onal
Probability

•  Independence
and
Incompa6bility

Natural
Language
Processing/Language
Technology

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Lecture 3 Probability Theory

1. Machine Learning for Language Technology Lecture 3: Probability Theory Marina San6ni Department of Linguis6cs and Philology Uppsala University, Uppsala, Sweden Autumn 2014 Acknowledgement: Thanks to Prof. Joakim Nivre for course design and materials

2. Outline •  Sta6s6cal methods and Natural Language Processing/ Language Technology •  No6on of Probability •  Sample Spaces •  Events •  Axioms of Probability •  Theorems of Probability •  Condi6onal Probability •  Independence and Incompa6bility

3. Sta6s6cal Methods…

4. Natural Language Processing/Language Technology

5. The No6on of Probability

6. Sample Spaces

7. Events

8. Composite Experiments

9. Axioms of Probability

10. Simple Probability: Examples

11. Theorems of Probability

12. Condi6onal Probability

13. Example 1: Dice

14. Example 2: Words