学习资源(压箱货 ☺️)

CS, HTML, CSS, Javascript, JQuery, PHP, ML, MCMC

Posted by Chaoli Zhang on September 1, 2017

The materials that I found particularly useful when I first dip into the field. I posted them up here for two reasons: 1> I can quickly re-access them whenever/wherever I want, 2> some of those might be helpful to you as well.

Of course, if you have anything better, please do not hesitate to share it by commenting at the bottom of the page.

Happy learning and studying!


Computer Science

A short crash course on computer science by Carrie Anne, videos can be found here:


HTML, CSS, Javascript, JQuery and PHP

A short introductory course on HTML, CSS and Javascript by EJ, videos can be found here:

My study notes, if you’re interested in having a look, can be found:


Machine Learning (ML)

  1. Scikit-learn
  2. Python Data Science Handbook by Jake VanderPlas
  3. A Friendly Introduction to Machine Learning from Udacity by Luis Serrano
  4. Computer vision for dummies 【very friendly article written here】
  5. Machine learning from mathematicalmonk 【Very math】
  6. Practical Machine Learning Tutorial with Python by Sentdex 【YouTube series】

  1. Principal Component Analysis (PCA)
  2. A Geometric way of understanding covariance matrix
  3. Spectral clustering
  4. Cluster Analysis
  5. Support Vector Machine algorithm
  6. Neural Network
  7. Naive Bayes Algorithm
  8. Genetic algorithm
  9. Tree decision
  10. Intuitive explanation of Convolutional Neural Networks

Markov Chain Monte Carlo (MCMC)

If you are a newbie, read those three blogs will help tremendously for your later understanding in mathematical properties of Markov Chain.

  1. Frequentism and Bayesianism by Jake Vanderplas
  2. My Tryst With MCMC Algorithms
  3. MCMC sampling for dummies
  4. Bayesian Statistics: A Beginner’s Guide by Michael Halls-Moore

  1. Introduction to PyMC scipy2014
  2. Bayesian Methods for Hackers
  3. Probable Points and Credible Intervals: Graphical Summaries
  4. SAS/STAT(R)1
  5. SAS/STAT(R)2
  6. Interpretation of autocorrelation plot in MCMC
  7. Markov Chain Monte Carlo (MCMC) to a layperson, haha, like me

Here are some links to the PDFs, which could be somehow mathematical, but I believe you guys can handle it 😏

  1. Introduction to Markov Chain Monte Carlo Charles J. Geyer
  2. The Conjugate Prior for the Normal Distribution
  3. Bayesian Inference: Metropolis-Hastings Sampling Ilker Yildirim (MIT)
  4. Monte Carlo Methods and Importance Sampling Berkeley
  5. Chapter 9: Equilibrium auckland