Questions covered

Question type	Description	Questions using this type
"definition"	tests definitional knowledge
"calculation"	the answer needs to be derived by means of calculation
"problem-solving"	the question requires reasoning beyond calculation
"training"	the question is about training particular models
"evaluation"	the question is about evaluating particular models

Question difficulty	Guideline	Questions of this difficulty
1	can be answered by looking up a specific passage in the book (e.g., match the following impurity measures with their mathematical definition)
2	requires some straightforward reasoning or calculation (e.g., complete the following contingency table)
3	requires more involved reasoning or calculation (e.g., a question involving some training data)
4	requires reasoning beyond what was discussed in the lectures (e.g., material on starred slides such as kernel methods)
5	a question on an advanced topic that was not discussed in the book (e.g., topics discussed in the epilogue)

Chapter	Chapter Name	Questions covering chapter
1	The ingredients of machine learning
2	Binary classification
3	Beyond Binary classification
4	Concept learning
5	Tree models
6	Rule models
7	Linear models
8	Distance-based models
9	Probabilistic models
10	Features
11	Model ensembles
12	Machine learning experiments