Dimensionality reduction Manifold learning Principal Component Analysis PCA

Dimensionality reduction manifold learning principal

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Dimensionality reduction / Manifold learning Principal Component Analysis (PCA) / Factor analysis Dictionary learning / Matrix factorization Kernel-PCA / Self organizing map / Auto-encoders Linear regression / Variable selection Least square regression / Ridge regression / Least absolute deviations LASSO / Sparse regression / Matching pursuit / Compressive sensing Classification and non-linear regression K-nearest neighbors Naive Bayes / Decision tree / Random forest Artificial neural networks / Support vector machines Quiz: Supervised or unsupervised? 98
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Machine learning – Quick overview of ML algorithms Quick overview of ML algorithms Clustering K-Means / Mixture models Hidden Markov Model Non-negative matrix factorization Recommendation Association rules Low-rank approximation Metric learning Density estimation Maximum likelihood / a posteriori Parzen windows / Mean shift Expectation-Maximization Simulation / Sampling / Generation Variational auto-encoders Deep Belief Network Generative adversarial network Often based on tools from optimization, sampling or operations research : Gradient descent / Quasi-Newton / Proximal methods / Duality Simulated annealing / Genetic algorithms Gibbs sampling / Metropolis-hasting / MCMC 99
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Questions? Next class: Preliminaries to deep learning Slides and images courtesy of Laurent Condat Rob Fergus Patrick Gallinari Naftali Harris Justin Johnson Andrej Karpathy Svetlana Lazebnik Fei-Fei Li Alasdair Newson Shibin Parameswaran David C. Pearson Steven Seitz Vincent Spruyt Vinh Tong Ta Ricardo Wendell Wikipedia 99
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