Reading questions

Machine learning questions:
  • What is a learning algorithm?
  • What is the difference between the training, validation and test sets?
  • What is supervised learning?
  • What is unsupervised learning?
  • What is empirical risk minimization?
  • What is stochastic gradient descent?
  • What is the capacity of a model?
  • What is the difference between a parametric and a non-parametric model?
  • What is the difference between a parameter and a hyper-parameter?
  • What is generalization?
  • What is the difference between under-fitting and over-fitting?
  • What is model selection?
  • What is regularization?
  • What is the bias variance trade-off?

Differential calculus:
  • What is a derivative?
  • What is a partial derivative?
  • What is a gradient?
  • What is the chain rule?
  • What is a Hessian matrix?

Linear algebra review:
  • What is a matrix? What is a vector?
  • How to perform products between matrices and vectors?
  • What are the following properties of matrices?
    • identity matrix
    • diagonal matrix
    • transpose matrix
    • symmetric matrix
    • orthogonal matrix
    • positive semidefinite matrix
  • What are the following operations on a matrix and their properties?
    • trace of a matrix
    • norm of a matrix
    • inverse of a matrix
    • determinant of a matrix
  • What is the concept of linear dependance between vectors?
  • What is the rank of a matrix?
  • What are the range and nullspace of a matrix?
  • What are the eigenvalues and eigenvectors of a matrix, and what are their properties?
  • How does the concept of gradient generalize to vectors and matrices?

Probability review
  • What is a probability space?
  • What is a random variable?
  • What is a distribution?
  • What is are joint, marginal and conditional distributions?
  • When are random variables independent?
  • What is the probability chain rule?
  • What is the Bayes rule?
  • What is the difference between a discrete and continuous distribution?
  • What is are density and cumulative distribution functions?
  • What are the expectation and variance of a random variable?
  • What are the Bernoulli, Poisson and Gaussian distributions?
  • What is Jensen's Inequality?

Statistics review:
  • How to estimate the expectation or variance of a random variable from samples?
  • What is a confidence interval?

Sampling review:
  • What is a simple Monte Carlo estimate?
  • What is direct sampling?
  • What is rejection sampling?
  • What is importance sampling?
  • What is Markov Chain Monte Carlo?
  • What is Metropolis Hastings?
  • What is Gibbs sampling?

  • What is the Python interpreter?
  • How to manipulate numbers and strings in Python?
  • What is the syntax of control flow statements (if, for, etc.) in Python?
  • How to use the common data structures (Lists, Tuples, Sets and Dictionaries) in Python?
  • How to define functions and classes?
  • What are modules and packages?
  • How to read and write in files?
  • What is and how to use Pickle (and cPickle)?
  • How to do linear algebra with Numpy?
  • How to debug in Python?

  • How to download and load a dataset?
  • What are MLProblems and how to manipulate them?
  • What are Learners?
  • How to perform a machine learning experiment with MLPython?