This presentation was provided by William Mattingly of the Smithsonian Institution, for the second session of NISO's 2023 Training Series on Text and Data Mining. Session two, "Data and Machine Learning," was held on Thursday, October 19, 2023.

1. Text and Data Mining
Data and Machine Learning

2. 1. What is Machine Learning?
2. How Data is used with Machine Learning
3. Types of Machine Learning
4. Challenges of Machine Learning
5. Ethical and Machine Learning
Goals

3. What is
Machine
Learning?
The use of a computer system to
learn from data via algorithms. The
goal of machine learning is to take
an input and produce a prediction,
or output.

4. We humans learn from experiences.

8. The _____ ran after the car.

9. The dog ran after the car.

10. The elephant ran after the car.

11. The snowman ran after the car.

12. Can computers do this?

13. Can inanimate objects do this?

14. Can boxes of matches do this?

15. MENACE
www.wikipedia.org

16. Donald Michie (d. 2007)
www.wikipedia.org

17. MENACE
www.wikipedia.org

18. The _____ ran after the car.

19. The dog ran after the car.

20. Generative AI

21. Exercise 1:
Draw an image of a squirrel that is part AI
robot and part snowman. It should also be
delivering a pizza on a beach.

22. an image of a squirrel that is part AI robot and part snowman. He should also be delivering a pizza on a beach.

23. an image of a squirrel that is part AI robot and part snowman. He should also be delivering a pizza on a beach.

24. an image of a squirrel that is part AI robot and part snowman. He should also be delivering a pizza on a beach.

25. Data and
Machine
Learning
Categorical
Images
Texts

27. Categorical Data

28. Types of
Machine
Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning

29. Supervised
Learning Using labeled data to train a model
to make a prediction.

30. Supervised
Learning Using labeled data to train a model
to make a prediction.

32. Classification
Text Classification
Classification

33. Classification
Span Classification
Michael went to the Eiffel Tower
when he visited Paris.

34. Classification
Image Classification

35. Classification
Object Detection

36. Unsupervised
Learning We have unlabeled data and we
want to see how that data relates to
other unlabeled data.

38. Challenges of
Machine
Learning Data
Computational Resources

39. Ethics and
Machine
Learning

40. Ethics and
Machine
Learning
-
- Environmental
- Human Labor
- Data

41. Python and
Machine
Learning
Scikit-Learn
Pytorch and FastAI
Tensorflow and Keras
spaCy
Ultralytics YOLO