Introduction to Computational Thinking and Data Science

15 Episodes

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Lecture 15: Statistical Sins and Wrap Up

2017-05-1044:43

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

Lecture 14: Classification and Statistical Sins

2017-05-1049:25

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

Lecture 13: Classification

2017-05-1049:53

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

Lecture 12: Clustering

2017-05-1050:39

Prof. Guttag discusses clustering.

Lecture 11: Introduction to Machine Learning

2017-05-1051:30

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

Lecture 10: Understanding Experimental Data (cont

2017-05-1050:33

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

Lecture 9: Understanding Experimental Data

2017-05-1047:05

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.

Lecture 8: Sampling and Standard Error

2017-05-1046:45

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

Lecture 7: Confidence Intervals

2017-05-1050:28

Prof. Guttag continues discussing Monte Carlo simulations.

Lecture 6: Monte Carlo Simulation

2017-05-1050:04

Prof. Guttag discusses the Monte Carlo simulation, Roulette

Lecture 5: Random Walks

2017-05-1049:20

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

Lecture 4: Stochastic Thinking

2017-05-1049:49

Prof. Guttag introduces stochastic processes and basic probability theory.

Lecture 3: Graph-theoretic Models

2017-05-1050:11

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

Lecture 2: Optimization Problems

2017-05-1048:04

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

Lecture 1: Introduction and Optimization Problems

2017-05-1040:56

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.

#box-pro-ellipsis-171346707911874{-webkit-line-clamp:2;}Introduction to Computational Thinking and Data Science

Introduction to Computational Thinking and Data Science