📗 Enter your ID (the wisc email ID without @wisc.edu) here: and click (or hit the "Enter" key) 1,2,3,4,5,6,7,8,9,10,11p6
📗 The official deadline is August 15, late submissions within a week will be accepted without penalty, but please submit a regrade request form: Link.
📗 The same ID should generate the same set of questions. Your answers are not saved when you close the browser. You could either copy and paste or load your program outputs into the text boxes for individual questions or print all your outputs to a single text file and load it using the button at the bottom of the page.
📗 Please do not refresh the page: your answers will not be saved.
📗 You should implement the algorithms using the mathematical formulas from the slides. You can use packages and libraries to preprocess and read the data and format the outputs. It is not recommended that you use machine learning packages or libraries, but you will not lose points for doing so.
📗 (Introduction) You can use any dataset you prefer, including the datasets you used in a previous homework or from another course. You have to implement at least one machine learning or search algorithm from the list below. This homework will be graded manually after the final exam.
📗 (Part 1) The list of algorithms you can build from scratch:
(1) Neural network with more than two hidden layers.
(2) Support vector machines.
(3) Bayesian network.
(4) Gaussian mixture model.
(5) Reinforcement Q-learning.
(6) Minimax with alpha-beta pruning.
📗 (Part 1) The list of algorithms you can build with a machine learning package:
📗 (Part 2) Please submit a short report to explain what you did. Your report should include the following sections:
(1) Introduction: explain the algorithm, the dataset, and the problem you are solving.
(2) Algorithm: explain if you made any modifications to the algorithm from the lecture slides.
(3) Dataset: explain if you did any preprocessing on the dataset.
(4) Result: explain the performance of algorithm on the dataset and anything else interesting you found.
(5) References and attributions.
📗 (Part 2) Please submit the code you wrote for the project. Please do not include the packages and libraries you used. Please do not include the dataset.
📗 [3 points] You can suggest a grade out of 30 for the previous three questions and provide an explanation (not an essay, please, I will look at your report too).
📗 [1 points] Please confirm that you are going to submit the code on Canvas under Assignment P6, and make sure you give attribution for all blocks of code you did not write yourself (see bottom of the page for details and examples).
📗 [1 points] Please enter any comments and suggestions including possible mistakes and bugs with the questions and the auto-grading, and materials relevant to solving the question that you think are not covered well during the lectures. If you have no comments, please enter "None": do not leave it blank.
📗 Please do not modify the content in the above text field: use the "Grade" button to update.
📗 Warning: grading may take around 10 to 20 seconds. Please be patient and do not click "Grade" multiple times.
📗 You could submit multiple times (but please do not submit too often): only the latest submission will be counted.
📗 Please also save the text in the above text box to a file using the button or copy and paste it into a file yourself . You can also include the resulting file with your code on Canvas Assignment P6.
📗 You could load your answers from the text (or txt file) in the text box below using the button . The first two lines should be "##p: 6" and "##id: your id", and the format of the remaining lines should be "##1: your answer to question 1" newline "##2: your answer to question 2", etc. Please make sure that your answers are loaded correctly before submitting them.
📗 Saving and loading may take around 10 to 20 seconds. Please be patient and do not click "Load" multiple times.