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Machine learning course materials.
| Date | Stars |
|---|---|
| 2026-07-31 | 585 |
| 2026-08-06 | 586 |
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<!-- # DS-GA 1003: Machine Learning and Computational Statistics --> <!-- - New figures illustrating regularization paths in space of all functions.--> ## Notable Changes Since [2018](https://davidrosenberg.github.io/ml2018/#home) - Added a [note](https://davidrosenberg.github.io/mlcourse/Notes/svm-retraining-with-support-vectors.pdf) on retraining SVMs with just the support vectors - Added a [note](https://davidrosenberg.github.io/mlcourse/Notes/logistic-regression-moment-matching.pdf) on a moment-matching interpretation of fitting logistic regression and more general softmax-style linear conditional probability models. ## Notable Changes from [2017FOML](https://bloomberg.github.io/foml/#home) to [2018](https://davidrosenberg.github.io/ml2018/#home) - Elaborated on the [case against sparsity](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/03a.elastic-net.pdf#page=18) in the lecture on elastic net, to complement the reasons *for* sparsity on the slide [Lasso Gives Feature Sparsity: So What?](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/02c.L1L2-regularization.pdf). - Added a [note on conditional expectations](https://davidrosenberg.github.io/mlcourse/Notes/conditional-expectations.pdf), since many students find the notation confusing. - Added a [note on the correlated features theorem for elastic net](https://davidrosenberg.github.io/mlcourse/Notes/elastic-net-theorem.pdf), which was basically a translation of Zou and Hastie's 2005 paper "Regularization and variable selection via the elastic net." into the notation of our class, dropping an unnecessary centering condition, and using a more standard definition of correlation. - Changes to EM Algorithm presentation: Added [several diagrams](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/13c.EM-algorithm.pdf#page=10) (slides 10-14) to give the general idea of a variational method, and made explicit that the marginal log-likelihood is exactly the pointwise supremum over the variational lower bounds [(slides 31 and 32)](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/13c.EM-algorithm.pdf#page=31)). - Treatment of [the representer theorem](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/04c.representer-theorem.pdf) is now well before any mention of kernels, and is described as an interesting consequence of basic linear algebra: "Look how the solution always lies in the subspace spanned by the data. That's interesting (and obvious with enough practice). We can now constrain our optimization problem to this subspace..." - The [kernel methods](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/05a.kernel-methods.pdf) lecture was rewritten to significantly reduce references to the feature map. When we're just talking about kernelization, it seems like unneeded extra notation. - Replaced the [1-hour crash course in Lagrangian duality](https://davidrosenberg.github.io/mlcourse/Archive/2017/Lectures/4a.convex-optimization.pdf) with a [10-minute summary of Lagrangian duality](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/04d.lagrangian-duality-in-ten-minutes.pdf), which I actually never presented and left as optional reading. - Added a [brief note on Thompson sampling for Bernoulli Bandits](https://davidrosenberg.github.io/mlcourse/in-prep/thompson-sampling-bernoulli.pdf) as a fun application for our [unit on Bayesian statistics](https://davidrosenberg.github.io/mlcourse/Archive/2018/Lectures/08a.bayesian-methods.pdf). - Significant improvement of the programming problem for lasso regression in [Homework #2](https://davidrosenberg.github.io/mlcourse/Homework/hw2.pdf). - New written and programming problems on logistic regression in [Homework #5](https://davidrosenberg.github.io/mlcourse/Homework/hw5.pdf) (showing the equivalence of the ERM and the conditional probability model formulations, as well as implementing regularized logistic regression). - New homework on backp
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7385c9d361412613, llm:Repository topics: course-materials, machine-learning; description and README indicate "Machine learning course materials" with lecture notes and course content.