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Mathematics for Machine Learning: PCA

Imperial College London via Coursera

Overview

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge.

The lectures, examples and exercises require:
1. Some ability of abstract thinking
2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis)
3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization)
4. Basic knowledge in python programming and numpy

Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms.

Taught by

Marc P. Deisenroth

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Reviews

2.0 rating, based on 3 reviews

Start your review of Mathematics for Machine Learning: PCA

  • Profile image for DietCoke
    DietCoke

    DietCoke is taking this course right now, spending 5 hours a week on it and found the course difficulty to be medium.

    I have completed the first 2 courses in the specilization, and this is the 3rd and the last course in the specilization. Everything was very easy until the last week of the last course. But when the hard part comes, the lecturer does not give proofs/explanation in detail, and the questions remain unanswered in the forum for months. Generally speaking, if you know the subject before the course, you will learn nothing; if you do not know the subject before taking the course, you won't understand it by taking the course etiher unless you do research yourself; you will remember the conclusion but not how to derive it, which I believe is undesirable in a MATHEMATICS course. Not recommend to devote either your time or money to it.

  • Anonymous

    Anonymous completed this course.

    Although the topics and lecturer's delivery were nice, but as compared to the two previous courses of the specialization, this one doesn't fare well. The content in the video lessons and that in the notebook were not really planned well in terms of scope. A participant who isn't already familiar with these concepts, would struggle a lot. Only if the reading material, video content and notebook assignments were designed keeping that in mind, it would have been better. Apart from that it was a good course. For some additional resources refer to this link:

    https://www.linkedin.com/posts/sagar-ladhwani-713b96112_datascience-machinelearning-mathematics-activity-6647131251033628672-6Qkm
  • Sagar Ladhwani

    Sagar Ladhwani completed this course and found the course difficulty to be hard.

    Although the topics and lecturer's delivery were nice, but as compared to the two previous courses of the specialization, this one doesn't fare well. The content in the video lessons and that in the notebook were not really planned well in terms of scope. A participant who isn't already familiar with these concepts, would struggle a lot. Only if the reading material, video content and notebook assignments were designed keeping that in mind, it would have been better. Apart from that it was a good course. For some additional resources refer to this link:

    https://www.linkedin.com/posts/sagar-ladhwani-713b96112_datascience-machinelearning-mathematics-activity-6647131251033628672-6Qkm

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