Basic Tools

Probabilistic Modeling & Causality

Probability helps us to understand the world.

• Bayesian Learning / Probabilistic Graphical Model
  • Bishop C. Pattern recognition and machine learning. Springer google schola, 2006, 2: 531-537.
  • Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press, 2009.
• Information Theory
  • Cover T M. Elements of information theory. John Wiley & Sons, 1999.
  • MacKay D J C. Information theory, inference and learning algorithms. Cambridge university press, 2003.
• Causal Inference
  • Pearl J. Causality: Models, Reasoning and Inference. Cambridge university press, 2009.

Numerical Compuation

Matrix helps us to process large-scale data, and optimization provides us with methods to solve problems.

• Numerical Analysis / Matrix Computation
  • Sauer T. Numerical analysis. Addison-Wesley Publishing Company, 2011.
  • Golub G H, Van Loan C F. Matrix computations. JHU press, 2013.
• Numerical Optimization
  • J Nocedal, SJ Wright. Numerical optimization. New York, NY: Springer New York, 1999.

Algebra & Graph theory

Algebra gives us the language of the mind.

• Linear Algebra
  • Axler S. Linear algebra done right. Springer Nature, 2023.
• Spectral Graph theory
  • Spielman D. Spectral graph theory. Combinatorial scientific computing, 2012, 18: 18.

Programming

Programming is an art.

• Programming Methodology
  • Abelson H, Sussman G J. Structure and interpretation of computer programs. The MIT Press, 1996.
• Best Practice
  • Bentley J. Programming pearls. Addison-Wesley Professional, 2016.