0 x00 preface

The first week mainly covers four parts:

  1. Introduction to Machine Learning
  2. Univariate linear regression model and cost function
  3. Gradient descent of univariate linear regression
  4. Line generation basis

Due to the length, the subsequent notes will be divided into several notes according to some topics. This article has only two parts: introduction to machine learning and linear algebra.

0x01 Introduction to Machine Learning

What is machine learning

The field of study that gives computers the ability to learn without being explicitly programmed.

The first definition of machine learning comes from Arthur Samuel. He defined machine learning as the field of giving computers the ability to learn in the context of specific programming.

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Closer to another s definition, put forward by Tom Mitchell, from Carnegie Mellon university, Tom is defined by machine learning, a good learning problems are defined as follows, he says, a program is supposed to learn from experience E, T solve tasks, achieve performance measurement value P, if and only if, after a experience E, after P evaluation, The performance of the program is improved in handling T.

2. Supervised learning

In supervised learning, we get a data set, we already know what our correct output should look like, and we think there is a relationship between the inputs and the outputs.

Supervised learning problems are divided into “regression” and “classification”.

In regression problems, we are trying to predict the outcome in a continuous output, which means we are trying to map the input variable to some continuous function.

In the classification problem, we try to predict the results in the discrete output. In other words, we are trying to map input variables to discrete categories.

3. Unsupervised learning

In unsupervised learning, we can have little or no idea what our results should look like.

Clustering is unsupervised learning. Take news clustering.

0x02 Line generation base

The basic content is not recorded in detail, only a few small points.

1. Basic Concepts

  • “Scalar” is a number
  • Verctor: N x 1 matrix
  • Matrix: Matrix
  • Identity Matrix: the Identity Matrix, which is 1 on the diagonal

Second, the operation

Matrix multiplication:

Matrix transpose:

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