Machine Learning and Inductive Inference
| Courses and exams | |
|---|---|
| Prof | Thiery Wim |
| Courses | Lectures and exercises |
| Examination | Oral and written exam Reports |
| Background | |
| Credits | 3 |
| When? | 1st semester |
| ECTS | VUB |
Since 2021-2022 this course was made available for a broader range of study programs.
Exams from before 2021-2022 can be found at the Wina exam wiki
2022
Januari
26/01/22
Small questions:
- connect 6 concepts with the right scentence about the concept or their definition
- same as previous question
- 3 questions where you have to anwser what the concept is.
- Which of the three has the largest entropy? {a,a,a,a,a,a} OR {a,b} OR {a,a,a,a,a,b}
Big questions:
- 12 examples given where 4 attributes(2 or three possible values per attribute) predict 1 class (3 possible values). A new instance their 4 attributes are given.
- Classify the new instance according to naive bayes (m=2, q=1/2)
- Classify the new instance according 4NN. (calculate the distance from the examples).
- The hypothesis "Tony likes pizza" is formulated with 0, 1 or 2 literals at most. The possible attributes that predict the hypothesis are {mushrooms, ham, tomatoes, onion}. Example that is valid"TOny likes pizza with mushrooms and tomatoes" or for 0 literals "Tony likes all pizza". Invalid is "TOny likes pizza with mushrooms or ham".
- Show that the VC dimension of this hypothesis space is at most 3.
- Show that the VC dimension of this hypothesis space is at least 3.
- Given 2 literals (something like: p(X,X) <-- q(X,Y)... and p(X,Y) <-- ... (both had a total of 3) )
- How many variables after combining them to claculate the LGG?
- What is the LGG (multiple choice a-g)
- Calculate the ROC based on the DT below. The proportions in the leaves discribe how the training data is labeled. Assume that a new instance will get classified as positive if the proportion of positives in the leaf is higher then a threshold C. Let the threshold C vary from 0-1 and draw the ROC for this descision tree classifier.