Contains exercises to implement the following algorithms: decision tree, naive Bayes, and AdaBoost. In the decision tree exercise, students will implement all mentioned attribute selection methods that are discussed in our lecture.
I also fixed errors in the examples of naive Bayes which come to my attention while implementing naive Bayes myself.
Closes issue #20.
Additionally, this PR includes minor changes to the lecture template and exercise archive builder which I will describe shortly:
lecture template: Added small footnote regarding prohibited recording of any third-party.
exercise archive builder: Moved main part to Python's "main" function.
I am aware that conventionally these are different topics and typically should not be shipped in one PR. Let me know if i should move them to another branch and open separate PRs for these.
Ideally, this PR is merged without squashing as commits are small enough and are disjunct already.
Contains exercises to implement the following algorithms: decision tree, naive Bayes, and AdaBoost. In the decision tree exercise, students will implement all mentioned attribute selection methods that are discussed in our lecture. I also fixed errors in the examples of naive Bayes which come to my attention while implementing naive Bayes myself.
Closes issue #20.
Additionally, this PR includes minor changes to the lecture template and exercise archive builder which I will describe shortly:
I am aware that conventionally these are different topics and typically should not be shipped in one PR. Let me know if i should move them to another branch and open separate PRs for these.
Ideally, this PR is merged without squashing as commits are small enough and are disjunct already.