3.1: Introduction to Session 3 – What is Machine Learning?
Machine learning overview
Common Algorithm （Machine Learning Recipe）：
Types of learning
Tech the system with the input and outputs
Have training data
Have test data (the data excludes from training data)
Have Samples (Unknown data)
Data that know nothing about *
e.g. songs patterns
Identify the commonalities in the data
3.2: Linear Regression with Ordinary Least Squares Part 1 – Intelligence and Learning
A linear approach to modelling the relationship between a scalar response
Ordinary Least Squares (OLS)「最小二乗法」
a type of linear least squares methods for estimating the unknown parameters in a linear regression model.
Linear Model formula:
formula to find the slop:
(where and are the average)
issue with different data sets
When to use linear regression?
- Residual plot should be random
3.4 Linear Regression with Gradient Descent
Gradient Descent 「最急降下法」
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
- partical derivative
Basic steps to implement the gradient descent
- Obtain the results from the correct result sets
- Obtain the evaluated results from the program
- Calculate the error (where the error is the bias from program result to the correct results)
- Adjusted the program parameter according to the learning rate (here is where the calculus comes in)