Machine Learning and Data Science are being used these days in a variety of applications including, but not limited to, forecasting in economics and finance, predicting anomalies or signal analysis in engineering, identification of speaker in acoustics, detection of cosmic bubbles in astrophysics and diagnosis in medical imaging.

While machine learning and data science have enabled many success stories, and tools are readily available to analyse data or design machine learning systems, the strong mathematical foundations in these areas are of significant importance to understand, review, analyse and evaluate the technical details of the machine learning systems and data science algorithms that are usually abstracted away from the user. This course focuses on the mathematical foundations that are essential to build an intuitive understanding of the concepts related to Machine Learning and Data Science.

Topics covered are

Linear Algebra: vectors and matrices, vector spaces, system of linear equations, eigen-value decomposition, singular value decomposition, regression, least-squares, regularization

Calculus: Multivariate calculus and differentials for optimization, gradient descent

Probability: probability axioms, Bayes rule, random variable, probability distributions