Mathematical Methods for Data Analysis (edX)

Mathematics has been playing an important role in data analysis from the very beginning; for example, Fourier analysis is one of the main tools in the analysis of image and signal data. This course is to introduce some mathematical methods for data analysis. It will cover mathematical formulations and computational methods to exploit specific structures contained in the data. Some special machine learning algorithms are introduced in case studies.

This course is part of the Big Data Technology MicroMasters program.

What you'll learn

- Vector spaces, metrics and convergence

- Case study: Clustering, k-means, k-medians

- Inner product, Hilbert space

- Case study: Kernel trick, kernel k-means; metrics learning

- Linear functions and differentiation

- Case study: Regression and classification; optimality and gradient descent

Syllabus

Chapter 1: Introduction to mathematical analysis tools for data analysis

Chapter 2: Vector spaces, metics and convergence

Chapter 3: Inner product, Hilber space

Chapter 4: Linear functions and differentiation

Chapter 5: Linear transformations and higher order differentations