assignments:assignment3
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assignments:assignment3 [2016/09/15 14:49] asa [Submission] |
assignments:assignment3 [2016/09/15 14:53] asa [Grading] |
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| should generate all the tables/plots used in your report. | should generate all the tables/plots used in your report. | ||
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| ===== Grading ===== | ===== Grading ===== | ||
| - | Here is what the grade sheet will look like for this assignment. A few general guidelines for this and future assignments in the course: | + | A few general guidelines for this and future assignments in the course: |
| - | * Always provide a description of the method you used to produce a given result in sufficient detail such that the reader can reproduce your results on the basis of the description (UNLESS the method has been provided in class or is there in the book). Your code needs to be provided in sufficient detail so we can make sure that your implementation is correct. The saying that "the devil is in the details" holds true for machine learning, and is sometimes the makes the difference between correct and incorrect results. If your code is more than a few lines, you can include it as an appendix to your report, or submit it as a separate file. Make sure your code is readable! | + | * Your answers should be concise and to the point. |
| - | * You can provide results in the form of tables, figures or text - whatever form is most appropriate for a given problem. | + | * You need to use LaTex to write the report. |
| + | * The report is well structured, the writing is clear, with good grammar and correct spelling; good formatting of math, code, figures and captions (every figure and table needs to have a caption that explains what is being shown). | ||
| + | * Whenever you use information from the web or published papers, a reference should be provided. Failure to do so is considered plagiarism. | ||
| + | |||
| + | We will take off points if these guidelines are not followed. | ||
| + | |||
| + | * Always provide a description of the method you used to produce a given result in sufficient detail such that the reader can reproduce your results on the basis of the description. You can use a few lines of python code or pseudo-code. | ||
| + | * You can provide results in the form of tables, figures or text - whatever form is most appropriate for a given problem. There are no rules about how much space each answer should take. BUT we will take off points if we have to wade through a lot of redundant data. | ||
| * In any machine learning paper there is a discussion of the results. There is a similar expectation from your assignments that you reason about your results. For example, for the learning curve problem, what can you say on the basis of the observed learning curve? | * In any machine learning paper there is a discussion of the results. There is a similar expectation from your assignments that you reason about your results. For example, for the learning curve problem, what can you say on the basis of the observed learning curve? | ||
| - | * Write succinct answers. We will take off points for rambling answers that are not to the point, and and similarly, if we have to wade through a lot of data/results that are not to the point. | ||
| <code> | <code> | ||
| - | Grading sheet for assignment 2 | + | Grading sheet for assignment 3 |
| Part 1: 50 points. | Part 1: 50 points. | ||
| - | (20 points): Plots of MAD and RMSE as a function of lambda are generated correctly. | + | (15 points): Ridge regression is correctly implemented. |
| - | (20 points): REC curves are generated correctly | + | (15 points): Plots of RMSE as a function of lambda are generated correctly. |
| - | ( 5 points): discussion of REC curves | + | (20 points): Discussion of the results |
| - | ( 5 points): Discussion of the MAD and RMSE plots and comparison of results to the published ones. | + | |
| - | Part 2: 40 points. | + | Part 2: 25 points. |
| - | (30 points): Weight vector analysis | + | (15 points): REC curves are generated correctly |
| - | (10 points): Comparison to the published results | + | (10 points): discussion of REC curves |
| + | |||
| + | Part 3: 25 points. | ||
| + | (20 points): Weight vector analysis | ||
| + | ( 5 points): Comparison to the published results | ||
| - | Report structure, grammar and spelling: 10 points | ||
| - | (10 points): Heading and subheading structure easy to follow and clearly divides report into logical sections. | ||
| - | Code, math, figure captions, and all other aspects of the report are well-written and formatted. | ||
| - | Grammar, spelling, and punctuation. | ||
| </code> | </code> | ||
| + | |||
assignments/assignment3.txt · Last modified: 2016/09/20 09:34 by asa
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