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?

 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!

 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

 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.

John's View

Bill's View

Machine's View