- Acknowledgement: We expect you to make an honest effort to solve the problems individually. As we sometimes reuse problem set questions from previous years, covered by papers and web pages, we expect you NOT to copy, refer to, or look at the solutions in preparing your answers (relating to an unauthorized material is considered a violation of the honor principle). Similarly, we expect you not to google directly for answers (though you are free to google for knowledge about the topic). If you do happen to use other material, it must be acknowledged in your submission.
- Required homework submission format: You should directly write your codes and answers in attached jupyter notebook files. Pay attention to the comments and instructions to see which parts need to be changed exactly. The teaching assistant will grade your assignment mainly based on the rightness of your programming implementation and rationality of your analytical answers.
- Collaborators: If you collaborated with others on any questions, list the questions and names of collaborator. Even if you acknowledge your collaborators, your solution should be written completely using your own words.
- In PA2, you need to apply Bernoulli naive Bayes model on a binary classification task. You will see one jupyter notebook file named
naive_bayes.ipynb.
- For implementation problems, you need to write your codes by directly editing the file. If you have trouble to deal with jupyter notebook, ask TA (wlsdzyzl@163.com) for help. And you are expected to only use numpy packages to implement the algorithms.
- For analytical questions, you can choose to write your answers in
naive_bayes.ipynb, or submit another seperate pdf file or image. We recommend you to use LaTeX, but you can also choose to write the answer by hand and take a picture.
- After finishing your assignment, you should pack all related files into one zip file. Note that
naive_bayes.ipynb must be included. Then submit it to the THU's web learning page.