Introduction

Introduction#

Welcome to the Advanced data science Section!

Here would will build on the previous MQB sections to learn about important techniques in advanced data analysis and statistics.

Here is a list of the prerequisite computing and mathematics skills and topics you need to be at least somewhat familiar with:

Prerequisites#

Maximum Likelihood and Bayesian Methods in Ecology and Evolution#

  1. Understanding and analysing functions (e.g., as a ``height’’ over a parameter space).

  2. Probability Theory

    • Manipulating probability distributions (joint, marginal, conditional) - Integrating PDF’s.

    • Familiarity with key distributions (e.g., Gaussian, Binomial, Poisson).

  3. Frequentist Hypothesis Testing

    • Understanding null and alternative hypotheses.

    • Interpreting p-values and test statistics.

  4. Differential Equations

    • Setting up and analyzing first-order ordinary differential equations (ODEs).

Machine Learning / AI for Ecology and Evolution#

All of the above, and in addition,

  1. Function Minimization and Optimization

    • Knowing what it means to minimize a function (e.g., sum of squared errors).

    • Understanding how parameters are searched in an optimization process.

  2. Linear Algebra: Eigenvectors and Eigenvalues

    • Familiarity with the definitions and significance of eigenvectors and eigenvalues.

  3. Computing and Software Installation Skills

    • Ability to troubleshoot R/Python environment setups.

    • Installing and configuring libraries like Keras/TensorFlow in Linux/UNIX.

Contents