Data Science in Julia for Hackers
Introduction
Open source
Prologue
Table of contents
Part I: Data Science and Julia
Part II: Bayesian Statistics
Part III: Machine Learning
Part IV: Deep Learning
Part V: Scientific Machine Learning
Part VI: Time Series and Forecasting
I Data Science and Julia
1
Science technology and epistemology
1.1
The difference between Science and Technology
1.2
What is technology?
1.3
References
2
Meeting Julia
2.1
Why Julia
2.2
Julia presentation
2.3
Installation
2.4
First steps into the Julia world
2.5
Julia’s Ecosystem: Basic plotting and manipulation of DataFrames
2.5.1
Plotting with Plots.jl
2.5.2
Introducing DataFrames.jl
2.6
Summary
2.7
References
2.8
Give us feedback
II Bayesian Statistics
3
Probability introduction
4
Spam filter
4.1
Naive Bayes: Spam or Ham?
4.2
Summary
4.3
References
4.4
Give us feedback
5
Probabilistic programming
6
Escaping from Mars
6.1
Calculating the constant g of Mars
6.2
Optimizing the throwing angle
6.2.1
Calculating the escape velocity
6.3
Summary
6.4
Give us feedback
7
Football simulation
8
Basketball shots
9
Optimal pricing
III Deep Learning
10
Image classification
IV Scientific Machine Learning
11
Ultima online
12
Ultima continued
13
Time series
V Epilogue
Epilogue
Data Science in Julia for Hackers
Data Science in Julia for Hackers
Chapter 5
Probabilistic programming