MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
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.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag explains dynamic programming and shows some applications of the process.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson
Prof. Grimson discusses graph models and depth-first and breadth-first search algorithms.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag introduces stochastic processes and basic probability theory.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses how to build simulations and plot graphs in Python.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses the Monte Carlo simulation, Roulette
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag continues discussing Monte Carlo simulations.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses sampling and how to approach and analyze real data.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson
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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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson
Prof. Grimson continues on the topic of modeling experimental data.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson
In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses clustering.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag finishes discussing classification and introduces common statistical fallacies and pitfalls.
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag continues the conversation about statistical fallacies and summarizes the take-aways of the course.
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Lecture 6: Vision 2 Instructor: John Gabrieli View the complete course: http://ocw.mit.edu/9-00SCS11 License: Creative Commons BY-NC-SA
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