Lecture 03
With document classification (e.g., spam filtering) as a motivating example, we reviewed issues with kNN for high-dimensional classification problems—namely the curse of dimensionality—and explored...
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Our previous discussion of naive Bayes led us to the problem of overfitting, specifically in dealing with rare words for text classification. We investigated this problem a bit more formally in the...
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In this lecture we studied maximum likelihood inference for linear classifiers. We saw that ordinary least squares regression can be phrased as maximum likelihood inference under the assumption of...
View ArticleHomework 02
The second homework is posted. The first problem is an exercise in using cross-validation to select the best-fit polynomial for some synthetic data where both the degree and coefficients are unknown....
View ArticleLecture 06
In this lecture we looked at non-linear feature transformations to accomodate more complex decision boundaries, introduced regularization to avoid overfitting, and covered the kernel trick for learning...
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Lecture 07
In this lecture we extended our toolbox of linear classification methods to include support vector machines (SVMs). We began with a unifying view of loss functions for classification, including...
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We had our first guest lecture this week. John Myles White presented recent work on modeling data from functional MRI experiments to understand the relationship between various mental states and...
View ArticleFinal Project
Here are details for the final project. The main objective is to apply techniques we’ve discussed in class to real-world data. You may either use a pre-compiled data set or build your own data set...
View ArticleLecture 09
In this lecture we discussed recommendation systems in general, and collaborative filtering in particular. With the Netflix Prize as a motivating example, we saw that simple memory-based methods (e.g.,...
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This week we had our second guest lecture. Max Shron presented a live demo of using Google Transit data to analyze the effects of budget cuts on passenger wait times, adapting his original analysis for...
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