What is Reinforcement Learning?

Environment => State, Reward





Agent =>Action

005. Machine learning W1 05 Model Representation

Linear regression with one variable
Model representation

Supervised Learning
Given the "right answer" for each example in the data.

Regression Problem
Predict real-valued output


Training set




Learning Algorithm







Size of house ->       h         -> Estimated price

x              hypothesis                 (estimated value of y)

h maps from x's to y's


How do we represent h?

Linear regression with one variable.
Univariate linear regression.


Linear regression with one variable
Cost function

004. Machinelearing W1 04UnsupervisedLearning

Unsupervised Learning

- Organize computing clusters
- Social network analysis
- Market segmentation
- Astronomical data analysis


Cocktail party problem

Speaker1                    Microphone1




Speaker2                     Microphone2


[W,s,v] = svd((repmat(sum(x.*x,1),size(x,1),1).*x)*x');


Of the following examples, which would you address using an unsupervised learning algorithm?(Check all that apply.)

- Given email labeled as spam/not spam, learn a spam filter.

- Given a set of news articles found on the web, group them into set of articles about the same story.


- Give a database of customer data, automatically discover market segments and group customers into different market segments.

- Give a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.

003. Machine learning _W1_03_Supervised Learning

Introduction Supervised Learning


Housing price prediction.

Supervised Learning
"right answers" given



Regression: Predict continuous
valued output(price)


Breast cancer(malignant, benign)








Classification
Discrete valued output(0 or 1)

- Clump Thickness
- Uniformity of Cell Size
- Uniformity of Cell Shape
...

You're running a company, and you want to develop learning algorithms to address each of two problems.

Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.

Problem 2: You'd like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.

Should you treat these as classification or as regression problems?

a) Treat both as classification problems.

b) Treat problem 1 as a classification problem, problem 2 as a regression problem.

c) Treat problem 1 as a regression problem, problem 2 as a classification problem.  T

d) Treat both as regression problems.

001. Machine learning 01_Welcome

Machine Learning
 - Grew out of work in AI
 - New capability for computers

Example:
 - Database mining
    Large datasets from growth of automation/web.
    E.g., Web click data, medical records, biology, engineering

 - Applications can't program by hand.
    E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing(NLP), Computer Vision.

 - Self-customizing programs
    E.g., Amazon, Netflix product recommendations

 - Understanding human learning(brain, real AI).


Machine Learning definition
- Arthur Samuel(1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.

- 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?

a) Classifying emails as spam or not spam.              T

b) Watching you label emails as spam or not spam.         E

c) The number(or Fraction) of emails correctly classified as spam/not spam.    P

d) None of the above-this is not a machine learning problem.     



Machine learning algorithms:

-  Supervised learning
-  Unsupervised learning


Others: Reinforcement learning, recommender systems.

Also talk about: Practical advice for applying learning algorithms.