Pattern Recognition and Machine Learning(PRML) 1

What is PRML?

PRML is a book about mathematics in machine learning. 

I read in turn with my friends using Translated one. Translated one is divided into two. We read Volume 1 in this summer vacation (2018).

Below is the topics of Volume 1

Chapter 1: Introduction

Chapter 2: Probability distribution

Chapter 3: Linear regression model

Chapter 4: Linear discriminant model

Chapter 5: Neural net work

Why I read this book with my friends

I read this for three reasons.

Firstly, I wanted to know the application example of the mathematics. I like math but I did not know how it is used.

Secondly, I wanted to know the inside of the artificial intelligence. AI is recently used in a lot of systems in the society. However, almost everyone do not know what AI can do and what AI cannot do. I thought that knowing inside is the best way to know it.

Thirdly, I wanted to improve my stamina for reading difficult books. I thought however difficult the book is, reading with friends would help me to complete it.

What I did 

I made a presentation for chapter 2, which is about probability distribution.

What I learnt

As I wrote before, my goal is

  • know the application example of mathematics
  • know what AI can do and cannot do
  • have stamina for reading a difficult book

First one and third one were accomplished. I studied a lot from my friends’ attitude for reading between lines. If I read it alone, I would give it up to read between lines at some places.

In terms of mathematics, we used Euler-Lagrange equation a lot. It was first time for me to do bayesian estimation, so I had some troubles to understand the flow of solving the optimization problems. In addition, I felt the lack of knowledge about the linear algebra. In the university classes. I learnt some operations of matrix but I did not know when it is used. It was great opportunity for learning it to read this book.


We are going to read volume 2 in spring vacation in 2019. I wish I could accomplish the second aim, “learning what AI can do and cannot do”.



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