• Vector and matrix arithmetics (1/6/2022)
    Defining and using vectors and matrices. Dot products between vectors and matrix multiplications.
  • Programming basics (Optional) (1/13/2022)
    Python programming skills needed for this course, Computational Core, and Cognitive Core.
  • Linear algebra (1/20/2022)
    Basic concepts in linear algebra: matrix multiplication, building up to applied topics such as Principal Components Analysis (PCA) and convolutions.
  • Probabilistic thinking (2/3/2022)
    Probability distributions and where they come about in analyzing data. Concepts in marginal and conditional probability. Common probability distributions, such as Gaussian, Bernoulli, Poisson, etc. Bayesian probability.
  • Calculus (2/17/2022)
    Calculus topics building up to differential equations, dynamical systems analysis and gradients.
  • Statistics (3/3/2022)
    Fundamental concepts such as variance, standard error, and significance. Parametric hypothesis testing methods, such as t-tests and ANOVA. Bootstrapping. Cross validation.