📊 MATHSTATS
This course covers numerical problem formulation, method selection, solution accuracy estimation, floating-point numbers, condition numbers, equation solving, convergence rates, interpolation, poly…
This course covers the theory behind classical numerical methods (for example: Newton-Raphson, Runge-Kutta and LU factorisation), and uses Matlab as a tool to solve, analyse and visualise computati…
This course includes multivariate location and scatter, principal component analysis (PCA), robustness and robust PCA, bivariate correspondence analysis, multiple correspondence analysis (MCA), can…
This course covers random vectors and random processes, Markov chains, branching processes, random point patterns and Poisson processes, population models, queues, and gambling
This course covers discrete and continuous distributions, expectation, sample mean, law of large numbers, variance, sample variances, normal approximation. stochastic dependence and correlation, de…
This course covers convexity, computable second derivatives, linear constraints, linear systems, minimization, gradient descent, and randomized large-scale optimization methods. Julia programming l…
This course covers time series analysis, linear regression model and its diagnostics, central concepts of weakly stationary processes, ARIMA-models and their properties, stationarity of ARIMA-model…
This course covers derivatives, integrals, basic differential equations, multivariable functions and optimization, double and triple integrals, variable changes in multiple integrals, cylindrical a…
This course covers logic, sets, functions, relations, combinatorics, induction and recursion, modular arithmetic, permutation groups and symmetry groups, graphs