data analysis foundations of - github pages · 2020-04-29 · foundations of data analysis lecture...
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![Page 1: Data Analysis Foundations of - GitHub Pages · 2020-04-29 · Foundations of Data Analysis Lecture 1: Introduction. 1/17/2019 Lecture 1: ... Levels of data analysis expertise 0: What](https://reader036.vdocuments.net/reader036/viewer/2022062920/5f0286667e708231d404b1c4/html5/thumbnails/1.jpg)
1/17/2019 Lecture 1: Introduction
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Foundations ofFoundations of Data AnalysisData Analysis
Lecture 1: IntroductionLecture 1: Introduction
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1/17/2019 Lecture 1: Introduction
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1/17/2019 Lecture 1: Introduction
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Pierre-Simon Laplace (1749–1827)
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1/17/2019 Lecture 1: Introduction
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Births in ParisBirths in Paris1745 - 17701745 - 1770
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1/17/2019 Lecture 1: Introduction
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Births in ParisBirths in Paris1745 - 17701745 - 1770
251,527 Boys
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1/17/2019 Lecture 1: Introduction
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Births in ParisBirths in Paris1745 - 17701745 - 1770
251,527 Boys 241,945 Girls
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1/17/2019 Lecture 1: Introduction
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Are males born at a higher rateAre males born at a higher ratethan females?than females?
251,527 Boys 241,945 Girls
![Page 8: Data Analysis Foundations of - GitHub Pages · 2020-04-29 · Foundations of Data Analysis Lecture 1: Introduction. 1/17/2019 Lecture 1: ... Levels of data analysis expertise 0: What](https://reader036.vdocuments.net/reader036/viewer/2022062920/5f0286667e708231d404b1c4/html5/thumbnails/8.jpg)
1/17/2019 Lecture 1: Introduction
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Some possible statisticsSome possible statistics251,527 Boys 241,945 Girls
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1/17/2019 Lecture 1: Introduction
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Some possible statisticsSome possible statistics251,527 Boys 241,945 Girls
Di�erence: +9,582 Boys
![Page 10: Data Analysis Foundations of - GitHub Pages · 2020-04-29 · Foundations of Data Analysis Lecture 1: Introduction. 1/17/2019 Lecture 1: ... Levels of data analysis expertise 0: What](https://reader036.vdocuments.net/reader036/viewer/2022062920/5f0286667e708231d404b1c4/html5/thumbnails/10.jpg)
1/17/2019 Lecture 1: Introduction
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Some possible statisticsSome possible statistics251,527 Boys 241,945 Girls
Di�erence: +9,582 Boys
Ratio: 104 Boys to 100Girls
![Page 11: Data Analysis Foundations of - GitHub Pages · 2020-04-29 · Foundations of Data Analysis Lecture 1: Introduction. 1/17/2019 Lecture 1: ... Levels of data analysis expertise 0: What](https://reader036.vdocuments.net/reader036/viewer/2022062920/5f0286667e708231d404b1c4/html5/thumbnails/11.jpg)
1/17/2019 Lecture 1: Introduction
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Some possible statisticsSome possible statistics251,527 Boys 241,945 Girls
Di�erence: +9,582 Boys
Ratio: 104 Boys to 100Girls
Proportion: 50.97% Boys
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1/17/2019 Lecture 1: Introduction
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Some possible visualizationsSome possible visualizations251,527 Boys 241,945 Girls
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1/17/2019 Lecture 1: Introduction
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How did Laplace solve this?How did Laplace solve this?
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1/17/2019 Lecture 1: Introduction
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How did Laplace solve this?How did Laplace solve this?Conditional Probability that:
rate of boys, , is greater than girls,
given
observed data
θ
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1/17/2019 Lecture 1: Introduction
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Answer?Answer?
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1/17/2019 Lecture 1: Introduction
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Answer?Answer?
where
P(θ > 0.5 ∣ data) = 1 − ϵ,
ϵ ≈ 1 × .10−42
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1/17/2019 Lecture 1: Introduction
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What is probability?What is probability?
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1/17/2019 Lecture 1: Introduction
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What is probability?What is probability?De�nition: Probability is the study of themathematical rules that govern random events.
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1/17/2019 Lecture 1: Introduction
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But what is randomness?But what is randomness?
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1/17/2019 Lecture 1: Introduction
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But what is randomness?But what is randomness?
Informally, a random event is an event where wedo not know the outcome without observing it.
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1/17/2019 Lecture 1: Introduction
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But what is randomness?But what is randomness?
Informally, a random event is an event where wedo not know the outcome without observing it.
Probability tells us what we can say about suchevents, given our assumptions about thepossible outcomes.
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1/17/2019 Lecture 1: Introduction
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What is statistics?What is statistics?
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1/17/2019 Lecture 1: Introduction
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What is statistics?What is statistics?De�nition: Statistics is the application ofprobability to the collection, analysis, anddescription of random data.
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1/17/2019 Lecture 1: Introduction
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Statistics is used to:
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1/17/2019 Lecture 1: Introduction
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Statistics is used to:
Design experiments
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1/17/2019 Lecture 1: Introduction
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Statistics is used to:
Design experiments
Summarize data
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1/17/2019 Lecture 1: Introduction
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Statistics is used to:
Design experiments
Summarize data
Make conclusions about theworld
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1/17/2019 Lecture 1: Introduction
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Statistics is used to:
Design experiments
Summarize data
Make conclusions about theworld
Explore complex data
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What is machine learning?What is machine learning?
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1/17/2019 Lecture 1: Introduction
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What is machine learning?What is machine learning?De�nition: Machine Learning builds statisticalmodels of data in order to recognize complexpatterns and to make decisions based on theseobservations.
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Machine Learning is Everywhere?
Chess player Recommendation system
Assisted driving Cancer diagnosis
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1/17/2019 Lecture 1: Introduction
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Levels of data analysisLevels of data analysisexpertiseexpertise
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1/17/2019 Lecture 1: Introduction
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Levels of data analysisLevels of data analysisexpertiseexpertise
0: What is data?
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1/17/2019 Lecture 1: Introduction
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Levels of data analysisLevels of data analysisexpertiseexpertise
0: What is data?1: I know how to run data analysis software
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1/17/2019 Lecture 1: Introduction
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Levels of data analysisLevels of data analysisexpertiseexpertise
0: What is data?1: I know how to run data analysis software2: I understand the math behind the analysis
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1/17/2019 Lecture 1: Introduction
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Levels of data analysisLevels of data analysisexpertiseexpertise
0: What is data?1: I know how to run data analysis software2: I understand the math behind the analysis3: I'm able to invent new data analysis methods
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1/17/2019 Lecture 1: Introduction
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Why should you know theWhy should you know themathematical foundations?mathematical foundations?
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1/17/2019 Lecture 1: Introduction
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When machine learning goes wrongWhen machine learning goes wrong
from Goodfellow et al. ICLR 2015
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1/17/2019 Lecture 1: Introduction
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When machine learning goes wrongWhen machine learning goes wrong
Panda (57.7% con�dence)
from Goodfellow et al. ICLR 2015
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When machine learning goes wrongWhen machine learning goes wrong
Panda (57.7% con�dence) Gibbon (99.3% con�dence)
from Goodfellow et al. ICLR 2015