This workshop presents the basics behind the application of modern machine learning algorithms. We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-fitting/under-fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data. The principles behind various algorithms—the why and how of using them—will be discussed, while some mathematical detail underlying the algorithms—including proofs—will not be discussed. Unsupervised machine learning algorithms presented will include k-means clustering, principal component analysis (PCA), and independent component analysis (ICA). Supervised machine learning algorithms presented will include support vector machines (SVM), classification and regression trees (CART), boosting, bagging, and random forests. Imputation, the lasso, and cross-validation concepts will also be covered. The R programming language will be used for examples, though participants need not have prior exposure to R.
Prerequisite: undergraduate-level linear algebra and statistics; basic programming experience (R/Matlab/Python).
- Basic Concepts and Intro to Supervised Learning: linear and logistic regression
- Penalties, regularization, sparsity (lasso, ridge, and elastic net)
- Unsupervised learning: clustering (k-means and hierarchical) and dimensionality reduction (Principal Component Analysis, Independent Component Analysis, Self-Organizing Maps, Multi-Dimensional Scaling)
- Unsupervised Learning: NMF and text classification (bag of words model)
- Supervised Learning: loss functions, cross-validation (bias variance trade-off and learning curves), imputation (K-nearest neighbors and SVD), imbalanced data
- Classification and Regression Trees (CART)
- Support Vector Machines (SVM) and Neural Nets
- Ensemble methods (Boosting, Bagging, and Random Forests)
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ICME Summer Workshops are open to participants 18 years and older. If you are under the age of 18 and would like to participate, please contact email@example.com.
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