Abstract: You will learn the basic concepts of machine learning – such as Modeling, Model Selection, Loss or Profit, overfitting, and validation – in a non-mathematical way, so that you can ask for data analysis and interpret the results of a model in the context of making business decisions. The concepts behind machine learning are actually quite simple, so expect to take away not just words and acronyms, but rather, a deep understanding. We will work in the context of concrete examples from different domains, including finance and medicine.
1. What is probability? What is a model? Supervised vs unsupervised learning. Regression and Classification. Minimizing Cost and Maximizing likelihood.
2. Models and Data: Bias, Variance, Noise, Overfitting, and how to solve Overfitting with Regularization and Validation
3. Different kinds of models, including ensembles and deep learning.
4. How good is a model? Profit Curves, ROC curves, and the expected value formalism.
Bio: Rahul Dave is a lecturer at Harvard University and partner at LxPrior, a small Data Science consultancy. LxPrior offers its clients data analysis services as well as data science training. Rahul trained as an astrophysicist, doing research on dark energy, and worked at the University of Pennsylvania, NASA’s Astrophysics Data System, as well as at Harvard University. As a computational scientist, he has developed time series databases, semantic search engines, and techniques for classifying astronomical objects. He was one of the people behind Harvard’s Data Science course CS109, and Harvard Library’s Data Science Training For Librarians course. This year he is teaching courses in computer science and stochastic methods to scientists and engineers.
