27-c LFD: Principal Components Analysis, PCA. скачать в хорошем качестве

27-c LFD: Principal Components Analysis, PCA.

Machine Learning From Data, Rensselaer Fall 2020. Professor Malik Magdon-Ismail talks about learning aides, add-ons that can help any learning technique. We start with input preprocessing, and move to PCA. We overview nonlinear dimensionality reduction, hints and invariances, de-noising and more advanced techniques for estimating out-of-sample error. This is the twenty-seventh lecture in a "theory" course focusing on the foundations of learning, as well as some of the more advanced techniques like support vector machines and neural (deep) networks that are used in practice. Level of the course: Advanced undergraduate, beginning graduate. Knowledge of probability, linear algebra, and calculus is helpful. Material is from e-Chapter 9 of "Learning From Data", amlbook.com, 2012.