Learn mathematical methods for data analysis including mathematical formulations and computational methods. Some well-known machine learning algorithms such as k-means are introduced in the examples.
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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
