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Mathematical Methods for Data Analysis

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.

...
Mathematical Methods for Data Analysis

There is one session available:

258 already enrolled!
After a course session ends, it will be archivedOpens in a new tab.
Starts Jun 29
Ends Aug 15

Mathematical Methods for Data Analysis

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.

Mathematical Methods for Data Analysis
Estimated 8 weeks
6–10 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts Jun 29
Ends Aug 15

About this course

Skip About this course

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.

At a glance

  • Institution: HKUSTx
  • Subject: Math
  • Level: Intermediate
  • Prerequisites:
    • Calculus
    • Linear algebra
  • Language: English
  • Video Transcript: English
  • Associated programs:

What you'll learn

Skip 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
  • 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

About the instructors

Who can take this course?

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