* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Machine Learning Introduction
Survey
Document related concepts
Inverse problem wikipedia , lookup
Theoretical computer science wikipedia , lookup
Regression analysis wikipedia , lookup
Algorithm characterizations wikipedia , lookup
Learning classifier system wikipedia , lookup
Renormalization group wikipedia , lookup
Simplex algorithm wikipedia , lookup
Mathematical optimization wikipedia , lookup
Backpropagation wikipedia , lookup
Types of artificial neural networks wikipedia , lookup
Least squares wikipedia , lookup
Generalized linear model wikipedia , lookup
Transcript
Machine Learning Introduction Study on the Coursera All Right Reserved : Andrew Ng Lecturer:Much Database Lab of Xiamen University Aug 12,2014 • Machine Learning - Grew out of work in AI(Artificial Intelligence) - New capability for computers • 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 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 email program watches which emails 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 emails as spam or not spam E : Watching you label emails as spam or not spam P: The number of emails 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. Supervised Learning & Unsupervised Learning x2 x1 Supervised Learning Unsupervised Learning Linear Regression with one Variable Housing Prices (Portland, OR) Price (in 1000s of dollars) Size (feet2) Supervised Learning Regression Problem Given the “right answer” for each example in the data. Predict real-valued output Training set of housing prices Size in feet2 (x) 2104 1416 1534 852 … Price ($) in 1000's (y) 460 232 315 178 … Notation: Training Set m = Number of training examples Learning Algorithm x’s = “input” variable / features y’s = “output” variable / “target” variable Size of house Question : How to describe h? h Estimated price Size in feet2 (x) 2104 1416 1534 852 … Training Set Hypothesis: ‘s: Parameters How to choose ‘s ? Price ($) in 1000's (y) 460 232 315 178 … 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 feet2 (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(0,1) 1 0 (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: Parameters: Cost function: Gradient descent: Repeat (simultaneously update for every ) New algorithm Repeat Gradient Descent : Previously (n=1): Repeat (simultaneously update ) (simultaneously update ) for Examples: 1 1 1 1 Size (feet2) Number of bedrooms Number of floors Age of home (years) Price ($1000) 2104 1416 1534 852 5 3 3 2 1 2 2 1 45 40 30 36 460 232 315 178 simultaneously update 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 !