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

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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