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Announcements
Assignment 1 may be found at this link: Assignment 1
Assignment 2 appears at this link: Assignment 2
Course Project requirements appear here: Project
Term test review materials appear at this link: Term Test Review
Lectures |
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Topics |
Description |
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Introduction |
Course overview, motivation and Math |
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Linear models |
Linear regression, least-squares, bias-variance tradeoff, overfitting |
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Dealing with Data |
Sourcing data, pre-processing, value of data, dimensionality, normalization |
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Non-Parametric Methods |
Clustering, Example based classification, kNN |
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Regression vs. Classification |
Classification as regression, logistic regression, SVMs, the kernel trick |
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Ensembles |
Boosting and bagging, decision trees, naive Bayes, random forests |
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Neural Networks I |
Perceptrons, Multi-layer perceptrons, Autoencoders |
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Neural Networks II |
Convolutional Neural Networks, Recurrent neural networks, GANs |
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Evaluation |
Performance evaluation, precision-recall, decision theory, accuracy, practial considerations |
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Statistical Models |
Probability distributions, maximum likelihood, MAP, parameter estimation, mixtures of Gaussians, EM algorithm |
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Graphical Models |
Bayes Nets, Markov Chains, Markov Random Fields, Hidden Markov Models, Sequence classification |
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