Domino: Creation of page, with exams from the Merkator wiki

2025-06-26T20:24:03Z

Creation of page, with exams from the Merkator wiki

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{{Infobox|data2=Thiery Wim|data3=Lectures and exercises|data4=Oral and written exam <br> Reports|data6=3|header1=Courses and exams|header5=Background|headerstyle=background:lightgrey|label2=Prof|label3=Courses|label4=Examination|label6=Credits|label7=When?|data7=1st semester|label8=ECTS|data8=[https://caliweb.vub.be/?page=course-offer&id=010402&anchor=1&target=pr&year=2425&language=en&output=html VUB]|title=Course Information}}

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 [https://wiki.wina.be/examens/index.php/Machine_Learning_and_Inductive_Inference 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.

Machine Learning and Inductive Inference - Bewerkingsoverzicht

Domino: Creation of page, with exams from the Merkator wiki