EE212 Mathematical Foundations for Machine Learning and Data Science
Summer 2020
Course Overview
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, eigenvalue decomposition, singular value decomposition, regression, leastsquares, regularization
Calculus: Multivariate calculus and differentials for optimization, gradient descent
Probability: probability axioms, Bayes rule, random variable, probability distributions
Statistics: descriptive stats, inferential stats, statistical tests
Introduction to Neural Networks: single and multilayer perceptron(s), feedforward and feedback networks
Application to machine learning and data science: principal component analysis (PCA), time series forecasting, clustering etc
Handson exercises: Implementation of the exercises will be carried out in MATLAB or Python
Announcements
Administrative Details
Grading Distribution
Lecture Notes and Exams
Lecture notes, past exams and reading assignmnets will be posted here.
Assignments
Tutorials
Lab Exercises
Quizzes
Lecture Plan
Lecture 01
Course Introduction
Fundamental concepts
Online Modules
