Tags: #python

Gradient descent for kernel learning, decision tree ensembles with bagging and boosting, PCA eigendigits, and k-means clustering on handwritten digits.

Linear SVM, least squares classifiers, Gaussian and Laplacian kernels, random Fourier feature approximation, and RKHS theory on MNIST digits.

Bayes optimal classifiers, k-NN error bounds, the curse of dimensionality in high dimensions, and VC dimension for disks and boxes.

Extracting mel-frequency cepstral coefficients from audio and using dynamic time warping for spoken digit recognition.

Implementing a Hidden Markov Model for POS tagging with supervised MLE, Viterbi decoding, and unsupervised Baum-Welch training.

Building FSTs to recognize poker hands and composing WFSTs for isolated digit speech recognition.

Building skip-gram and CBOW word2vec models with negative sampling, training on the Stanford Sentiment Treebank.

Building logistic regression from scratch in NumPy with gradient checking, feature scaling, and decision boundary visualization on exam data.