Introduction to

Machine Learing

The science of getting computers to act without being explicitly programmed.

-Stanford University CS 229 (Machine Learning) 

What is Machine Learning?

Inference and learning with massive datasets using intelligent machines.

-UBC CSPC340 (Machine Learning & Data Mining)

A branch of artificial intelligence that relies heavily on probability statistics uses data to make predictions and learn.

- The Economist

Get some insights from a process by which computer scientists direct a program to pore through a huge database.

- Harvard Business Review

Artificial Intelligence

Machine Learning

Data Mining

The Restaurant Case

John

Jack used to come a lot, why he hasn't showed up for a while?

Why Bill's bar is more popular?

Is Roy a slow server?

Vancouver's best selling beer

Average cost to run a restaurant

Transaction records related to Jack

......

Popular sauces

Popular restaurants

Query From Computer

Busiest hour of the week

Data Driven Decision

John

People like the happy hour in Bill's Bar so much that the keep going there even when it's not happy hour.

 

Roy is a great server, he sells more food, and that's why it seems like he is slow.

 

The Honey BBQ Rib is the best seller, and it has 10% chance of sold out during weekend. So get enough stock before weekend.

But,is that good enough?

But, Is that Good Enough?

 MachineLearning.py

 

 

import os

import scipy as sp

import matplotlib.pyplot as plt

 
print("I'm MachineLearning")
def ML(models, x, y):

 if models:

   if mx is None and x = 0

       for x, y in zip(models):

# print "Model:",model

# print "Coeffs:",model.coeffs

          plt.plot(model(x), c=color)

          plt.legend(["d=%i" % m.order for m in models], loc="upper left") Python Hi, Python.

Meh~

Look!

Machine Found A Pattern

 MachineLearning.py

 

 

import os

import scipy as sp

import matplotlib.pyplot as plt

 
print("I'm MachineLearning")
def ML(models, x, y):

 if models:

   if mx is None and x = 0

       for x, y in zip(models):

# print "Model:",model

# print "Coeffs:",model.coeffs

          plt.plot(model(x), c=color)

          plt.legend(["d=%i" % m.order for m in models], loc="upper left") Python Hi, Python.

Top Restaurants

 

Other Restaurants 

Factor 2

Factor 1

Machine Found Another Pattern

 MachineLearning.py

 

 

import os

import scipy as sp

import matplotlib.pyplot as plt

 
print("I'm MachineLearning")
def ML(models, x, y):

 if models:

   if mx is None and x = 0

       for x, y in zip(models):

# print "Model:",model

# print "Coeffs:",model.coeffs

          plt.plot(model(x), c=color)

          plt.legend(["d=%i" % m.order for m in models], loc="upper left") Python Hi, Python.

Top Restaurants

 

Other Restaurants 

Factor 4

Factor 3

Let the Machine Decide 

 MachineLearning.py



import os

import scipy as sp

import matplotlib.pyplot as plt

 
print("I'm MachineLearning")
def ML(models, x, y):

 if models:

   if mx is None and x = 0

       for x, y in zip(models):

# print "Model:",model

# print "Coeffs:",model.coeffs

          plt.plot(model(x), c=color)

          plt.legend(["d=%i" % m.order for m in models], loc="upper left") Python Hi, Python.

Hey John, let's talk.

Factor 1, 34, 42443, 254432 & 4342343214 are the most significant factors. Align your strategies in those factors with the best restaurants. You can beat Bill's Bar in a week.

Machine Does It Better

KPI

View Span

John's View

Bill's View

Machine's View

“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.”

-- Tom Mitchell, Carnegie Mellon University

Chess Playing

Experience: playing chess games 10,000 times

 

Task: playing checkers

 

Performance measures: probability that win the play 

Machine Learning Algorithms

Supervised Learning

Unsupervised Learning

Supervised Learning

Right Answer Given

Unsupervised Learning

Learn by self

Organize computing clusters

social network analysis

market segmentation

astronomical data analysis

Machine Learning

is

everywhere

Customized Recommendation

Handwriting recognition

Face Recognition

Ranking webpages

Database Mining

Application cannot program by hand

Self-customizing program

 

Softwares

http://www.forbes.com/sites/85broads/2014/01/06/six-novel-machine-learning-applications/

 

http://www.wsj.com/articles/SB10001424052748703834604575365310813948080

ML

By Frank Qiu

ML

Machine Learning Presentation

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