Linear Algebra for Machine Learning Workshop

Linear Algebra, Optimization & Statistics is base of all machine learning. This workshop will cover required linear algebra for machine learning in a hands on way through short code examples. We will cover basic theory, interesting applications and the big picture.

 
 

Outline/Structure of the Workshop

This workshop will be in three parts. Overall idea is to first motivate audience on why linear algebra is important, give a big picture first rather than tiny details. Then we cover real applications through coding so that audience understands most important ideas in this topic. Lastly, we will cover enough details on how to study and apply it further.

Introduction / Getting feet wet

  1. Why is linear algebra so important and pervasive in machine learning.
  2. Basic code examples, linear algebra 101
  3. Motivation for some topics in linear algebra, why are they important (solving system, basis, orthogonalization, eigen values and eigen vectors, SVD)

Practical Applications through Coding

  1. Simple application: Regression models training and scoring
  2. Intermediate applications: Applications in optimization methods
  3. Advanced applications: Singular Value Decomposition, Principal component analysis, application to recommendations systems.

Practical Applications Discussion

In this section, depending on time we will discuss even more applications, some of them from other engineering disciplines (like control theory, signal processing) and physics and explain how they related to data science and machine learning. The idea is to go beyond the currently hyped topics in machine learning and explain the all encompassing reach of Linear Algebra in engineering.

Study Resources, Question Answers

  1. How to study further, books and courses which helped me so far.
  2. Open question answer session

Learning Outcome

By the end of workshop, you would

  1. know why linear algebra is important for machine learning.
  2. understand how the most important ideas/algorithms in linear algebra work and how to apply them in your work.
  3. understand how linear algebra relates to machine learning through hands on coding examples.
  4. increase your curiosity and respect for pervasiveness of linear algebra in data science.
  5. know which resources can help you study further.

Target Audience

Beginners in machine learning field who want to learn the maths/linear algebra in an intuitive way.

Prerequisites for Attendees

  • High school maths
  • Familiarity with basic machine learning like linear regression will help but not mandatory.
  • Programming knowledge (given below)
    • Simple imperative programming like assigning to values to variables, printing things
    • Loops, conditions
    • Reading and writing to/from files.
    • Basic data structures and data types: floating point numbers, arrays/vectors, maps
  • Familiarity with Python will help. There are many tutorials available online. This is a good place to start.
schedule Submitted 2 years ago

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