Prof. Guttag continues the conversation about statistical fallacies and summarizes the take-aways of the course.

Lecture 15: Statistical Sins and Wrap Up

Prof. Guttag finishes discussing classification and introduces common statistical fallacies and pitfalls.

Lecture 14: Classification and Statistical Sins

Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.

Lecture 13: Classification

Lecture 12: Clustering

In this lecture, Prof. Guttag introduces machine learning and shows examples of supervised learning using feature vectors.

Lecture 11: Introduction to Machine Learning

Prof. Grimson continues on the topic of modeling experimental data.

Lecture 10: Understanding Experimental Data (cont

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.

Lecture 9: Understanding Experimental Data

Prof. Guttag discusses sampling and how to approach and analyze real data.

Lecture 8: Sampling and Standard Error

Prof. Guttag continues discussing Monte Carlo simulations.

Lecture 7: Confidence Intervals

Prof. Guttag discusses the Monte Carlo simulation, Roulette

Lecture 6: Monte Carlo Simulation

Introduction to Computational Thinking and Data Science

Prof. Guttag discusses how to build simulations and plot graphs in Python.

Lecture 5: Random Walks

Prof. Guttag introduces stochastic processes and basic probability theory.

Lecture 4: Stochastic Thinking

Prof. Grimson discusses graph models and depth-first and breadth-first search algorithms.

Lecture 3: Graph-theoretic Models

Prof. Guttag explains dynamic programming and shows some applications of the process.

Lecture 2: Optimization Problems

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.

Lecture 1: Introduction and Optimization Problems

First timer interested in data science with self taught coding experience, and so far I am loving this course. Great insight, simple to understand, and moves at a nice pace. So glad I&#39;ve found this!

Great course

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