Lab 1: basic dot product, average, norm, rms, standard deviation, standardisation.
Lab 2 : vector representation, what we can use * for , plot vectors function, vector representation, how to simulate a dataset, correlation coefficient., different representations using de-meaned and standardised vectors
Lab 3: creating a sympy and numpy matrix, rref and finding rref of different files
Lab 5: plot vectors function, linsolve, plot vectors with parameters, scalar and vector projection.
Lab 6: plot vectors, meals sold problem, projections of meals sold in different directions, finding orthonormal basis set and graham Schmidt, projection plot or scatter plot for meals sold problem
Lab 7: plot vectors function, projections, linear dependency checks, Gram-Schmidt, linear combination of columns of a matrix
Lab 9: plotveccomp function, simulating a dataset, a few dot products and notations, ecg problem and many operations on it
Lab 11: matrix matrix multiplication the three versions, student data simulation and a few operations, circular shift matrix, power of a matrix, plotveccomp function, power consumption and ecg problem again.
Lab 12: working on the lion image, matrix matrix multiplication version 4, plotveccomp function