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

2. 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

3. 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.

4. 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. ”

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

6. x1x1 x2x2 Supervised Learning & Unsupervised Learning Supervised LearningUnsupervised Learning

7. 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

8. 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) 2104460 1416232 1534315 852178 …… Training set of housing prices Training Set Learning Algorithm h Size of house Estimated price Question : How to describe h?

9. How to choose ‘ s ? Training Set Hypothesis: ‘ s: Parameters Size in feet 2 (x)Price ($) in 1000's (y) 2104460 1416232 1534315 852178 ……

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

12. Cost Function Hypothesis: Parameters: Cost Function: Goal: Simplified:

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

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

15. Gradient descent algorithm Correct: Simultaneous update Incorrect:

16. Gradient descent algorithm Notice : α is the learning rate.

17. 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.

18. 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.

19. Gradient Descent for Linear Regression Gradient descent algorithm Linear Regression Model

20. Gradient descent algorithm update and simultaneously

21.   J(     )

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

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

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

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

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

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

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

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

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

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

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

33. Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) 121045145460 114163240232 115343230315 18522136178 Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) 21045145460 14163240232 15343230315 8522136178 Examples: simultaneously update Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) 21045145460 14163240232 15343230315 8522136178

34. 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 !