The document discusses machine learning algorithms for symbol-based learning. It introduces a general framework for symbol-based learning that includes representing data and goals, representation languages, operations for manipulating representations, concept spaces to search, and heuristics for guiding the search. It then describes two specific algorithms: version space search and ID3 decision tree induction. Version space search uses a candidate elimination algorithm to iteratively generalize from positive examples and specialize from negative examples to identify the target concept.

8. A general model of the learning process (Fig. 9.1)

10. 9.1 Framework for Symbol-based Learning

14. Examples and Near Misses for the concept “Arch” (Fig. 9.2)

15. Generalization of descriptions (Figure 9.3)

16. Generalizations of descriptions (Fig 9.3 continued)

17. Specialization of description (Figure 9.4)

21. A Concept Space (Fig. 9.5)

24. Specific to General Search

25. Specific to General Search (Fig 9.7)

26. General to Specific Search

27. General to Specific Search (Fig 9.8)

28. 9.2.2 The candidate elimination algorithm

30. 9.2.2 The candidate elimination algorithm (Fig. 9.9)

32. 9.2.2 The candidate elimination algorithm