Machine Learning

Frank Qiu

Haider Shah

Stephanie Zhang

Chris Pang

Machine Learning

Frank Qiu​

Chris Pang

Haider Shah

Stephanie Zhang

Intro

Algorithms

Business Value

Best Practices

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

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 Can do it Again and Again.

 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 Can do it Again and Again.

 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 Is Better

KPI

View Span

John's View

Bill's View

Machine's View

Artificial Intelligence

Machine Learning

Data Mining

Machine Learning Algorithms

Supervised Learning

Unsupervised Learning

Reinforced Learning

Supervised Learning

Right Answer Given

Unsupervised Learning

Learn by self

Organize computing clusters

social network analysis

market segmentation

astronomical data analysis

Reinforced Learning

robotics, gaming and navigation

 

discover through trial and error

 

three components: agent, environment and actions

Machine Learning

is

everywhere

Machine Learning

is

Everywhere

Customized

Recommendation

Face

Recognition

Handwriting

Recognition

Ranking

webpages

Applications

Big Data

can't program

by hand

Beyond

human

capability

 $48

billion

/year

Online Advertising

 $11.5

/year
billion

Fraud detection systems

Gene prediction for cancer

nature disaster prediction

self-driving cars 

...

  1. Data preparation capabilities.
     
  2. Algorithms – basic and advanced.
     
  3. Automation and iterative processes.
     
  4. Scalability.
     
  5. Ensemble modeling.

Best Practices

  1. GUIs for building models and interactive data exploration and visualization
     
  2. Automated model evaluation to identify the best performers.
     
  3. Easy model deployment so you can get repeatable, reliable results quickly.

Matching Tools to Processes

SAS

 

Python

 

Java

Software

http://machinelearningmastery.com/

 

https://www.coursera.org/course/ml

Further Learning

http://www.research.ibm.com/foiling-financial-fraud.shtml

http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf

http://www.nytimes.com/2015/02/12/technology/personaltech/googles-time-at-the-top-may-be-nearing-its-end.html

http://www.quora.com/What-are-some-interesting-possible-applications-of-machine-learning

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

 

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

Machine Learning - Mar 28 (Ver1)

By Christopher Pang

Source: Frank Qiu

Machine Learning - Mar 28 (Ver1)

Machine Learning Presentation

  • 1,430

More from Christopher Pang