Introduction To Machine Learning Self-Evaluation Test

Preface

Section 1 - Mathematical background

Multivariate calculus

1. take derivatives and integrals;
2. derive gradients of multivariate functions.

Linear algebra

1. multiply vector and matrices;
2. matrix inversion;
3. eigenvectors;
4. eigenvalues ;
5. matrix factorization.

probability and statistics

1. mean and variance;
2. common probability distribution : Gaussian and Uniform distribution;
3. conditional distribution and Bayes rule;
4. calculate the likelihood (probability)
5. deriving the parameters of the distribution

Section 2 - Usage

If pass "Modest Background Test" , you are good in shape of take the class.

If pass "Minimum Background Test" , but not the "Modest Background Test", then you can take the class but you should expect to devote extra time to fill in necessary math background.

Necessary Minimum Background Test

Multivariate calculus

partial derivative

Vectors and matrices

product
inverse
rank

Probability and statistics