Statistical Learning Examples Genevera I. Allen Statistics 640: Statistical Learning August 26, 2013 (Stat 640) Lecture 1 August 26, 2013 1 / 19
Example: Microarrays arrays High-dimensional: Goals: Measures gene expression. Often tens of thousands of genes. Only a couple hundred subjects. Find genes indicative of a certain disease. Predict survival outcomes based on gene expression. Estimate regulatory pathways. genes (Stat 640) Lecture 1 August 26, 2013 2 / 19
Example: Face Recognition Goal: Recognize facial expression. Goal: Recognize faces from a database. (Stat 640) Lecture 1 August 26, 2013 3 / 19
Example: Handwritten Digits (Stat 640) Lecture 1 August 26, 2013 4 / 19
Example: Computer Vision Object Recognition: (Le Cun et al., 2004) (Stat 640) Lecture 1 August 26, 2013 5 / 19
Example: Netflix Movie Rating Data Rows: Movies. Columns: Customers. Measurement: Movie ratings (scale of 1-5). Anne Ben Charlie Doug Eve... Star Wars 2 5 4 4 3... Harry Potter 3 4 5 3?... Pretty Woman 4? 2? 5... Titanic 5? 2 1 3... Lord of the Rings? 5 5 4 4............ (Stat 640) Lecture 1 August 26, 2013 6 / 19
Netflix Prize Challenge: Predict un-rated movies with 10% improvement over Cinematch. Training Set: 480,000 customer ratings on 18,000 movies. Around 98.7% missing ratings! $1,000,000 prize! Contest: October 2006 - August 2009. Winners: Team led by Robert Bell and Yehuda Koren. Methods: Variations on the SVD and k-nearest neighbors (Bell & Koren, 2008). Fields: Recommender systems & Collaborative filtering. (Stat 640) Lecture 1 August 26, 2013 7 / 19
My Research (Stat 640) Lecture 1 August 26, 2013 8 / 19
Modern Multivariate Analysis (Stat 640) Lecture 1 August 26, 2013 9 / 19
Example: NMR Spectroscopy Goal: Find chemical signatures to classify neural cells. (Stat 640) Lecture 1 August 26, 2013 10 / 19
Example: EEG Data Goal: Find major brain activation patterns. (Stat 640) Lecture 1 August 26, 2013 11 / 19
Example: fmri Data Goal: Find major patterns & areas of brain activation. (Stat 640) Lecture 1 August 26, 2013 12 / 19
Markov Networks: Models and Inference (Stat 640) Lecture 1 August 26, 2013 13 / 19
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Example: Integrated Genomic Networks Goal: Find links between different types of genomic biomarkers. (Stat 640) Lecture 1 August 26, 2013 15 / 19
Example: Functional Brain Networks Goal: Find functional relationships between remote brain regions. (Stat 640) Lecture 1 August 26, 2013 16 / 19
Example: Inference for Functional Brain Networks How do functional connections differ between two populations of subjects? (Stat 640) Lecture 1 August 26, 2013 17 / 19
What will we cover in this class? Linear Regression & Penalized Regression. Linear Classification & Penalized Classification. Linear Discriminant Analysis. Support Vector Machines. Kernel Methods. Model Selection & Assessment (cross-validation, stability selection). Matrix Factorizations: PCA, Sparse PCA, ICA, NMF. Matrix Completion. Markov Networks (undirected graphical models). Clustering - K-means, Hierarchical & Spectral. Boosting. Ensemble Methods. Decision Trees & Random Forests. (Stat 640) Lecture 1 August 26, 2013 18 / 19
What is not covered in this class? Wavelets, Basis Transformations, Kernel Smoothing. Splines & Functional Data Methods. Mixture Models. Soft Clustering. SOM, Isomap, Laplacian embedding. Semi-Supervised methods. Manifold Learning. Generalized Additive Models & MARS. Neural Networks. Boltzmann machines. Bayesian Networks. Variational Inference. Markov Decision Processes. Hidden Markov Models. (Stat 640) Lecture 1 August 26, 2013 19 / 19