Machine Learning Introduction Study on the Coursera All Right Reserved : Andrew Ng Lecturer:Much Database Lab of Xiamen University Aug 12,2014

Examples: - Database mining Large datasets from growth of automation/web. Web click data, medical records, biology, engineering - Applications can ’ t program by hand. Handwriting recognition, most of Natural Language Processing (NLP), Computer Vision. Machine Learning - Grew out of work in AI(Artificial Intelligence) - New capability for computers

Machine Learning Definition Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Suppose your program watches which s you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? T : Classifying s as spam or not spam E : Watching you label s as spam or not spam P: The number of s correctly classified as spam/not spam “ A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. ”

Machine Learning Algorithms -Supervised learning -Unsupervised learning -Others: - Reinforcement learning - Recommender systems.

x1x1 x2x2 Supervised Learning & Unsupervised Learning Supervised LearningUnsupervised Learning

Linear Regression with one Variable Housing Prices (Portland, OR) Price (in 1000s of dollars) Size (feet 2 ) Supervised Learning Given the “ right answer ” for each example in the data. Regression Problem Predict real-valued output

Notation: m = Number of training examples x ’ s = “ input ” variable / features y ’ s = “ output ” variable / “ target ” variable Size in feet 2 (x)Price ($) in 1000's (y) …… Training set of housing prices Training Set Learning Algorithm h Size of house Estimated price Question : How to describe h?

How to choose ‘ s ? Training Set Hypothesis: ‘ s: Parameters Size in feet 2 (x)Price ($) in 1000's (y) ……

y x Idea: Choose so that is close to for our training examples

Cost Function Hypothesis: Parameters: Cost Function: Goal: Simplified:

Price ($) in 1000 ’ s Size in feet 2 (x) Question:How to minimize J?

Gradient Descent Have some function Want Outline: Start with some Keep changing to reduce until we hopefully end up at a minimum

Gradient descent algorithm Correct: Simultaneous update Incorrect:

Gradient descent algorithm Notice : α is the learning rate.

If α is too small, gradient descent can be slow. If α is too large, gradient descent can overshoot the minimum. It may fail to converge, or even diverge.

at local optima Current value of Unchange Gradient descent can converge to a local minimum, even with the learning rate α fixed. As we approach a local minimum, gradient descent will automatically take smaller steps. So, no need to decrease α over time.

Gradient Descent for Linear Regression Gradient descent algorithm Linear Regression Model

Gradient descent algorithm update and simultaneously

  J(     )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

(for fixed, this is a function of x)(function of the parameters )

Linear Regression with multiple variables Hypothesis: Cost function: Parameters: (simultaneously update for every ) Repeat Gradient descent:

(simultaneously update ) Gradient Descent Repeat Previously (n=1): New algorithm : Repeat (simultaneously update for )

Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) Examples: simultaneously update Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000)

Summarize This is a briefly Introduction about Supervised Learning(Classification)in Machine Leaning. There is still a lot of things in this subject,such as Clustering, Support Vector Machine(SVM), Dimensionality Reduction, ETC. The Core Idea of MS is very similar,hope you will be fond of the Machine Learning ！ Thanks for Listening !