Prof. Guttag continues the conversation about statistical fallacies and summarizes the take-aways of the course.
Prof. Guttag finishes discussing classification and introduces common statistical fallacies and pitfalls.
Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.
In this lecture, Prof. Guttag introduces machine learning and shows examples of supervised learning using feature vectors.
Prof. Grimson continues on the topic of modeling experimental data.
Prof. Grimson talks about how to model experimental data in a way that gives a sense of the underlying mechanism and to predict behavior in new settings.
Prof. Guttag discusses sampling and how to approach and analyze real data.
Prof. Guttag continues discussing Monte Carlo simulations.
Prof. Guttag discusses the Monte Carlo simulation, Roulette
Prof. Guttag discusses how to build simulations and plot graphs in Python.
Prof. Guttag introduces stochastic processes and basic probability theory.
Prof. Grimson discusses graph models and depth-first and breadth-first search algorithms.
Prof. Guttag explains dynamic programming and shows some applications of the process.
Prof. Guttag provides an overview of the course and discusses how we use computational models to understand the world in which we live, in particular he discusses the knapsack problem and greedy algoriths.