PSYCH253

Introduction to high-dimensional data analysis and machine learning methods for use in the behavioral and neurosciences, including: supervised methods such as SVMs, linear and nonlinear regression and classifiers, and regularization techniques; statistical methods such as bootstrapping, signal detection, factor analysis, and reliability theory; metrics for model/data comparison such as representational similarity analysis; and unsupervised methods such as clustering. Students will learn how to both use existing statistical data analysis packages (such as scikit-learn) as well to build, optimize, and estimate their own custom models using an optimization framework (such as Tensorflow or Pytorch). Requirement: Psych 251. Familiarity with python programming and multivariable calculus and linear algebra (Math 51) highly recommended.