A Guide to Data Sciecne: from Mathematics to Algorithms

It is a blueprint to data science from the mathematics to algorithms. It is not completed. It is of my own interest. It is web-source based. And some material draw from the original owners' blogs. Last but not least, thanks to the teachers who guide me to mathematics. And the copyright of pictures are not permmited by the original oweners. If this material infringes your copyright, you could contact me via Github and I will delete the pictures.

I hope that it is on the ADEPT way like Kalid Azad

ADEPT Method for Learning
Analogy	Tell me what it is like
Diagram	Help me visualize it
Example	Allow me to experience it
Plain English	Describe it with everyday word
Technical Definition	Discuss the formal details

It is expected a part of A Guide to Data Science, where the overview of data science is roughly presented. It is used to attract the people to the world of data.

It includes the archicture, optimization methods and regularization, acceleration and compression of deep neural network. The state of the art is not discussed.

It includes not limited the following content:

As the name shown, it is a guide thus it does not cover all the methods or technques in data science or data mining. Deeper advanced topics such as probability correct algorithms are not discussed. Until now, it is web resource driven. There are many links on each topic but no concrete examples and codes.

The basic idea is machine learning = representation+evaluation + optimization. I would like formulate every machine learning problem into numerical optimization problems. The section Numerical optimmization may be too theoretical for practioner and too simple for the research on optimization.

It is draft and notes when I learn data science. It is planned to be a open source e-book.

Name		Name	Last commit message	Last commit date
Latest commit History 350 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
Acceleration.md		Acceleration.md
AutoML.md		AutoML.md
Back-propagation and beyond.md		Back-propagation and beyond.md
Bayesian Learning and Probabilistic Programming.md		Bayesian Learning and Probabilistic Programming.md
Bayesian learning.md		Bayesian learning.md
Bayesian learning.pdf		Bayesian learning.pdf
CNAME		CNAME
Cascaded Ranking Models.md		Cascaded Ranking Models.md
Classification and clustering.md		Classification and clustering.md
Classification_and_clustering.html		Classification_and_clustering.html
Computational intelligence.md		Computational intelligence.md
Computer vision.md		Computer vision.md
Data Structure In the Era of Big Data.md		Data Structure In the Era of Big Data.md
Decision tree and polynomials (1).md		Decision tree and polynomials (1).md
Decision tree and polynomials.md		Decision tree and polynomials.md
Decsion tree and ensemble methods.md		Decsion tree and ensemble methods.md
Decsion tree and ensemble methods.pdf		Decsion tree and ensemble methods.pdf
Deep Learning.md		Deep Learning.md
Dimension Reduction.md		Dimension Reduction.md
Discrete Optimization.md		Discrete Optimization.md
Embeding Methods.md		Embeding Methods.md
GCN.md		GCN.md
GLMs and More.md		GLMs and More.md
GLMs, GAMs and More.md		GLMs, GAMs and More.md
Generalized Linear Model.md		Generalized Linear Model.md
Geometric Deep Learning.md		Geometric Deep Learning.md
Graph Algorithms.html		Graph Algorithms.html
Graph Algorithms.md		Graph Algorithms.md
Hash.md		Hash.md
Information retriveal and web search.md		Information retriveal and web search.md
Least Squares and Variable Selection.md		Least Squares and Variable Selection.md
MCMC.md		MCMC.md
Machine Learning via a Modern Optimization Lens.md		Machine Learning via a Modern Optimization Lens.md
MathDL.md		MathDL.md
Metric Learning.Rmd		Metric Learning.Rmd
Metric-Learning.html		Metric-Learning.html
NLP.md		NLP.md
Numerical Optimization.html		Numerical Optimization.html
Numerical Optimization.md		Numerical Optimization.md
Numerical Optimization.pdf		Numerical Optimization.pdf
PCA.md		PCA.md
Polynomials.md		Polynomials.md
Probabilistic Programming and TDA.md		Probabilistic Programming and TDA.md
QuickScorer.html		QuickScorer.html
QuickScorer.md		QuickScorer.md
README.md		README.md
Rating and Ranking.md		Rating and Ranking.md
ReLU Networks.md		ReLU Networks.md
Recommender System and Computational Advertising.html		Recommender System and Computational Advertising.html
Recommender System and Computational Advertising.md		Recommender System and Computational Advertising.md
Recommender.md		Recommender.md
Recommender.pdf		Recommender.pdf
Regression Analysis and Machine Learning.md		Regression Analysis and Machine Learning.md
Science of Deep Learning.html		Science of Deep Learning.html
Science of Deep Learning.md		Science of Deep Learning.md
Statistical learning.html		Statistical learning.html
Statistical learning.md		Statistical learning.md
Statistical learning.pdf		Statistical learning.pdf
Stochastic Process.md		Stochastic Process.md
TDA.html		TDA.html
TDA.md		TDA.md
TDA.pdf		TDA.pdf
Transformer.md		Transformer.md
Tree-based and Ensemble methods.md		Tree-based and Ensemble methods.md
Tree-based and Ensemble methods.pdf		Tree-based and Ensemble methods.pdf
User Modeling.md		User Modeling.md
_config.yml		_config.yml
ds.md		ds.md
guide.pdf		guide.pdf
guide.tex		guide.tex
recommender.md		recommender.md
数据库.md		数据库.md
数据结构.md		数据结构.md

Hulalazz/A-_Guide_-to_Data_Sciecne_from_mathematics

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