Important Dates
 


Your Course Project
 

Your class project is an opportunity for you to explore an interesting machine learning problem in the context of a real-world data set. We are providing some seed project ideas below. You can pick one of these ideas, and explore the data and algorithms within and beyond what we suggest. You can also use your own data/ideas, but, in this case, you have to make sure you have the data available now and a nice roadmap, since a quarter is too short to explore a brand new concept.

Projects can be done by you as an individual, or in teams of two students. You can discuss your ideas and approach with the instructors, but of course the final responsibility to define and execute an interesting piece of work is yours.

The final project is worth 20% of your grade, which will be split amongst three deliverables:

Your project will be evaluated by three criteria:

The Technical Depth and Scope are complementary criteria, e.g., if you develop a single elaborate algorithm or model on a small dataset, you may score high on depth but low on scope, while if you try many very simple methods on different datasets, your scope would be higher but the depth lower.

 

Project Proposal
 

You must turn in a project proposal on Monday May 6th at 9:30am through Catalyst.

Read the list of available data sets and potential project ideas below. If you prefer to use a different data set, we will consider your proposal, but you must have access to this data already, and present a clear proposal for what you would do with it.
 

Project proposal format: Proposals should be one page maximum. Include the following information:

 

Project Milestone
 

A project milestone should be submitted on May 24th at 9:30am via Catalyst. Your write up should be 3 pages maximum in NIPS format, not including references (the templates are for LaTex, if you want to use other editors/options please try to get close to the same format). You should describe the results of your first experiments here. Note that, as with any conference, the page limits are strict! Papers over the limit will not be considered.

 

Poster Session
 

We will hold a poster session on June 7th from 2:00-4:00pm in the Atrium of the Paul Allen Center. Each team will be given a stand to present a poster summarizing the project motivation, methodology, and results. The poster session will give you a chance to show off the hard work you put into your project, and to learn about the projects of your peers.

Here are some details on the poster format:

 

Project Report
 

Your final submission will be a project report on June 10 at 9:30am via Catalyst. Your write up should be 8 pages maximum in NIPS format, not including references (the templates are for LaTex, if you want to use other editors/options please try to get close to the same format). You should describe the task you solved, your approach, the algorithms, the results, and the conclusions of your analysis. Note that, as with any conference, the page limits are strict! Papers over the limit will not be considered.

 

Project Ideas
 

The course staff has outlined several potential project ideas below. This should give you a sense of the datasets available and an appropriate scope for your project. You can either pick one of these or come up with something of your own to work on.

Netflix Challenge

From 2006-2009, Netflix sponsored a competition to improve its movie recommendation system. Their system is based off of predicting what rating a user will give to a particular movie (using a 1-5 star system). In effect, what we have is a matrix where each row represents a user and each column represents a movie. Some elements are filled with past ratings, but most of them are unknown. Students can use matrix factorization or clustering methods to predict the missing values in this matrix.

fMRI Brain Imaging

Brain scans were taken of a subject in the process of a word reading task. We want to be able to predict what word the participant is reading based off of the activation patterns in their brain. To do this, we have 218 semantic features for each word in our dictionary (where each feature is a rating from 1-5 answering a question such as "Is it an animal?"). Thus, we can use the fMRI image to predict the semantic features of the word, and then use our dictionary to find our best guess as to which word it is. In this way, we can predict words without ever having seen them in our training set.

Document Clustering

Documents taken from sources like Wikipedia or the online discussion board Usenet often have word choice that reflects the topic being dicussed -- for example, the word "clustering" is much more likely to show up in a document on Computer Science than one about motorcycles. Given the collection of words in a document, it should be possible to predict what subject it is contained in. Alternatively, we might want to try to find documents similar to the one in question: if someone is reading a Wikipedia article on classification, perhaps they would also like to know that there is an article on regression.

Digit Recognition

Implement handwriting recognition by classifying pictures (stored as pixel data) as the appropriate digit. This project is based off a tutorial ML competition hosted on kaggle.com

Federalist Papers

This task involves the famous disputed federalist papers. Some of the papers we know who wrote them and others we do not. The task involves converting the text into a "bag of words" (see attached for more info) feature vector and then attempting to classify the remaining essays as Hamilton or Madison. Students can use cross validation to test predictive power on known essays. This project involves mining text, which may be a challenging component, but it shows a very cool connection between machine learning and history.

Job Salary Prediction

This is another task taken from a Kaggle competition. Given an advertisement for a job opening, the goal is to predict the starting salary for the job being posted. Much of the data about the ads is unstructured text (like the ad content itself), but some structured data is given as well. A tree of the geographic relationships between the job locations is also provided. This task is similar to the running example in lecture of predicting starting salary, and has real-world usefulness to the company that posted the problem.

Eigenfaces

The goal of this task is to learn how to recognize faces. We have a set of pictures of 20 people in various directions and expressions, some of which have sunglasses. One major problem with image data is that our input features are individual pixels, which are high-dimensional but not terribly meaningful in isolation. Using PCA, we can decompose our images into eigenvectors, which are linear combinations of pixels (nicknamed "eigenfaces"). Students can explore different classification tasks, from determining the presence of sunglasses to identifying individuals.