Introduction to Computational Thinking and Data Science

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This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language.

John Guttag

May 10, 2017 LATEST EPISODE
infrequent NEW EPISODES
48m AVG DURATION
15 EPISODES

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Latest episodes from Introduction to Computational Thinking and Data Science

Lecture 15: Statistical Sins and Wrap Up

Play Episode Listen Later May 10, 2017 44:43

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

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Lecture 14: Classification and Statistical Sins

Play Episode Listen Later May 10, 2017 49:25

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

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Lecture 13: Classification

Play Episode Listen Later May 10, 2017 49:53

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

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Lecture 12: Clustering

Play Episode Listen Later May 10, 2017 50:39

Prof. Guttag discusses clustering.

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Lecture 11: Introduction to Machine Learning

Play Episode Listen Later May 10, 2017 51:30

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

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Lecture 10: Understanding Experimental Data (cont

Play Episode Listen Later May 10, 2017 50:33

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

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Lecture 9: Understanding Experimental Data

Play Episode Listen Later May 10, 2017 47:05

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.

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Lecture 8: Sampling and Standard Error

Play Episode Listen Later May 10, 2017 46:45

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

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Lecture 7: Confidence Intervals

Play Episode Listen Later May 10, 2017 50:28

Prof. Guttag continues discussing Monte Carlo simulations.

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Lecture 6: Monte Carlo Simulation

Play Episode Listen Later May 10, 2017 50:04

Prof. Guttag discusses the Monte Carlo simulation, Roulette

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Lecture 5: Random Walks

Play Episode Listen Later May 10, 2017 49:20

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

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Lecture 4: Stochastic Thinking

Play Episode Listen Later May 10, 2017 49:49

Prof. Guttag introduces stochastic processes and basic probability theory.

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Lecture 3: Graph-theoretic Models

Play Episode Listen Later May 10, 2017 50:11

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

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Lecture 2: Optimization Problems

Play Episode Listen Later May 10, 2017 48:04

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

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Lecture 1: Introduction and Optimization Problems

Play Episode Listen Later May 10, 2017 40:56

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.