Machine Learning Techniques (NTU, Spring 2018) instructor: Hsuan-Tien Lin
Final Project
TA email: ml2017ta@csie.ntu.edu.tw
RELEASE DATE: 05/17/2018
COMPETITION END DATE: 06/25/2018 NOON ONLINE REPORT DUE DATE: 07/02/2018 NOON ONLINE
Unless granted by the instructor in advance, no late submissions will be allowed.
Any form of cheating, lying, or plagiarism will not be tolerated. Students can get zero scores and/or fail the class and/or be kicked out of school and/or receive other punishments for those kinds of misconducts.
You should write your solutions in English or Traditional Chinese with the common math notations introduced in class or in the problems. We do not accept solutions written in any other languages.
Introduction
In this final project, you are going to be part of an exciting machine learning competition. Consider a company that runs book selling service. The key to a successful service is to predict whether a user would like a book or not. The prediction should be based on both the user and the book. Now, having collected some data from the service, the board of directors of the company decides to hold a competition and make the problem of book preference prediction open to experts like you. To win the prize, you need to fight for the leading positions on the score board. Then, you need to submit a comprehensive report that describes not only the recommended approaches, but also the reasoning behind your recommendations.
Well, let’s get started!
Data Sets
The problem is formalized as a regression problem, where the goal is to predict the rating “truth” of each (user, book) pair accurately. We will have two tracks of competition. The details of the tracks, which would differ by evaluation criteria (i.e. error functions), will be announced later. The data will be divided to the training set and the test set. For the test set, the rating “truth” will be hidden.
The data sets are processed from the book-crossing data along with some TA-crawled data. To maxi- mize the level of fairness, you are not allowed to download the original book-crossing data at any time.
You are also not allowed to use any data other than those provided. But you are welcomed to go check the descriptions of the original book-crossing data at
http://www2.informatik.uni-freiburg.de/~cziegler/BX/
Survey Report
You are asked by the board to study at least THREE machine learning approaches using the training set above. Then, you should make a comparison of those approaches according to some different perspectives, such as efficiency, scalability, popularity, and interpretability. In addition, you need to recommend THE BEST ONE of those approaches as your final recommendation for each track and provide the “cons and pros” of the choice.
The survey report should be no more than SIX A4 pages with readable font sizes. The most important criterion for evaluating your report is replicability. Thus, in addition to the outlines above, you should also describe how you pre-process your data, such as the features you build; introduce the approaches you tried and provide specific references, especially for those approaches that we didn’t cover in class; list your experimental settings and the parameters you used (or chose) clearly. Other criteria for evaluating your survey report would include, but are not limited to, clarity, strength of your reasoning, “correctness”
in using machine learning techniques, the work loads of team members, and properness of citations.
Our sincere suggestion: Think of your TAs as your boss who wants to be convinced by your report.
For grading purposes, a minor but required part in your survey report for a two- or three-people team (see the rules below) is how you balance your work loads.
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Machine Learning Techniques (NTU, Spring 2018) instructor: Hsuan-Tien Lin
Competition
The submission site would be announced later. Use your submissions wisely—you do not want to leave the board with a bad impression that you just want to “query” or “overfit” the test examples. After submitting, there will be a score board showing the test error on a random half of the data set. The
“hidden” test error on the other half will eventually be used to evaluate your performance.
The competition ends at noon on 06/25/2018. We’ll have a mini-ceremony to honor the best team(s) on 06/26/2018. The competition site will continue to be open until the due day of the report.
Misc Rules
Report: Please upload one report per team electronically on CEIBA. You do not need to submit a hard-copy. The report is due at noon on 07/02/2018.
Teams: By default, you are asked to work as a team of size THREE. A one-person or two-people team is allowed only if you are willing to be as good as a three-people team. It is expected that all team members share balanced work loads. Any form of unfairness, such as the intention to cover other members’ work, is considered a violation of the honesty policy and will cause some or all members to receive zero or negative score.
Algorithms: You can use any algorithms, regardless of whether they were taught in class.
Packages: You can use any software packages for the purpose of experiments, but please provide proper references in your report for replicability.
Source Code: You do not need to upload your source code for the final project. Nevertheless, please keep your source code until 08/01/2018 for the graders’ possible inspections.
Grade: The final project is worth 400 points. That is, it is equivalent to two usual homework sets. At least 360 of them would be reserved for the report. The other 40 may depend on some minor criteria such as your competition results, your discussions on the boards, your work loads, etc..
Collaboration: The general collaboration policy applies. In addition to the competitions, we still encourage collaborations and discussions between different teams.
Data Usage: You can use only the data sets provided in class for your experiments, and you should use the data sets properly. Getting other forms of the data sets is strictly prohibited and is considered a serious violation of the honesty policy. Using any tricks to query the labels of the test set is also strictly prohibited.
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