MATH 2060 MATH FOR DATA SCIENCE
Math for Data Science provides students with the
additional mathematical background (beyond the
prerequisite courses) needed for data science or
machine learning. This course will cover the
following areas:
. Stats/Prob: Probability distribution functions:
uniform, normal, binomial, chi-square, Student's
t-distribution, central limit theorem; Sampling,
measurement, error, random number generation;
Hypothesis testing for multivariables, A/B
testing, confidence intervals, p-values; ANOVA,
t-test; Linear regression, regularization
. Linear Algebra: Basic properties of matrix and
vectors: scalar multiplication, linear
transformation, transpose, conjugate, rank,
determinant; Inner and outer products, matrix
multiplication rule and various algorithms, matrix
inverse; Special matrices: square matrix, identity
matrix, triangular matrix, idea about sparse and
dense matrix, unit vectors, symmetric matrix,
Hermitian, skew-Hermitian and unitary matrices;
Matrix factorization concept/LU decomposition,
Gaussian/Gauss-Jordan elimination, solving Ax=b
linear system of equation; Vector space, basis,
span, orthogonality, orthonormality, linear least
square; Eigenvalues, eigenvectors,
diagonalization, singular value decomposition
. Calculus: Basics of Taylor's series, infinite
series summation/integration concepts; Beta and
gamma functions; Functions of multiple variables,
limit, continuity, partial derivatives; Basics of
ordinary and partial differential equations
. Optimization: Basics of optimization, how to
formulate the problem; Maxima, minima, convex
function, global solution?; Linear programming,
simplex algorithm; Integer programming; Constraint
programming, knapsack problem; Randomized
optimization techniques: hill climbing, simulated
annealing, genetic algorithms
LA
Prerequisite
Prerequisite: MATH.1030