Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
We will review some modern machine learning applications, understand variety of machine learning problem definitions, go through particular approaches of solving machine learning tasks.
This year 2015 Amazon and Microsoft introduced services to perform machine learning tasks in cloud. Microsoft Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments.
We will briefly review Azure ML Studio features and run machine learning experiment.
3. Abstract
Machine learning is the science of getting computers to act without being explicitly
programmed. In the past decade, machine learning has given us self-driving cars,
practical speech recognition, effective web search, and a vastly improved understanding
of the human genome. Machine learning is so pervasive today that you probably use it
dozens of times a day without knowing it.
We will review some modern machine learning applications, understand
variety of machine learning problem definitions, go through particular approaches
of solving machine learning tasks.
This year 2015 Amazon and Microsoft introduced services to perform machine
learning tasks in cloud. Microsoft Azure Machine Learning offers a streamlined
experience for all data scientist skill levels, from setting up with only a web browser, to
using drag and drop gestures and simple data flow graphs to set up experiments.
We will briefly review Azure ML Studio features and run machine learning
experiment.
4. Agenda
● Machine Learning definition
● Applications Overview
● Types of Problems and Tasks
● Machine Learning as a Service (Azure)
● Machine Learning experiment in Azure ML
Studio
6. Machine Learning
Machine Learning (ML)
focuses on the
development of
computer programs
that can teach
themselves to grow
and change when
exposed to new data.
7. Machine Learning vs Data Mining
Focuses on the
discovery of
(previously) unknown
properties in the data
Focuses on prediction,
based on known
properties learned from
the training data
22. Reinforcement
learning
Machine learning problem categories
Three broad categories, depending on the nature
of the learning "signal" or "feedback" available to
a learning system:
Supervised
learning
Unsupervised
learning
25. Supervised learning goal
The computer is presented with
example inputs and their desired
outputs, given by a "teacher", and
the goal is to learn a general rule
that maps inputs to outputs
26. Problem definition
x11 x12 … x1n
x21 x22 … x2n
x31 x32 … x3n
… … … …
xm1 xm2 … xmn
y1
y2
y3
ym
Training
Examples
“input” variable / features “output” variable
h(x) = h(x1, x2, …, xn) – hypothesis function and
solution of a supervised learning problem, where
h(x) ≈ y (as close as possible)
28. Linear regression
hθ(x) – linear
hypothesis function,
m – number of training
examples,
Θ – vector of
coefficients of the
linear function hθ(x)
Cost Function
31. Unsupervised learning
No labels are given to the learning algorithm.
The goal is to find hidden structure in its
input.
Since the examples given to the learner are
unlabeled, there is no error or reward
signal to evaluate a potential solution
39. Reinforcement learning
Performing a certain goal (such as
driving a vehicle) in a dynamic
environment, without a teacher
explicitly telling it whether it has come
close to its goal or not.
40. Reinforcement learning differences
No correct input/output pairs
Sub-optimal actions aren’t explicitly corrected
Instead it maximizes some notion of
cumulative reward
There is a focus on on-line performance
Finds a balance between exploration (of
uncharted territory) and exploitation (of
current knowledge)
41. Basic reinforcement learning model
a set of environment states
a set of actions
rules of transitioning between states
rules that determine the scalar immediate
reward of a transition
rules that describe what the agent observes
48. On April 15, 1912, during her
maiden voyage, the Titanic
sank after colliding with an
iceberg, killing 1502 out of
2224 passengers and crew.
https://www.kaggle.com/c/titanic
49. PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
1 0 3
Braund, Mr. Owen
Harris
male 22 1 0 A/5 21171 7.25 S
2 1 1
Cumings, Mrs. John
Bradley (Florence
Briggs Thayer)
female 38 1 0 PC 17599 71.2833 C85 C
3 1 3 Heikkinen, Miss. Laina female 26 0 0
STON/O2.
3101282
7.925 S
4 1 1
Futrelle, Mrs. Jacques
Heath (Lily May Peel)
female 35 1 0 113803 53.1 C123 S
5 0 3
Allen, Mr. William
Henry
male 35 0 0 373450 8.05 S
6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
rows
891
columns
12Titanic Dataset
https://www.kaggle.com/c/titanic/data
sibsp - Number of Siblings/Spouses Aboard
parch - Number of Parents/Children Aboard
embarked - Port of Embarkation:
C = Cherbourg
Q = Queenstown
S = Southampton
52. Useful Links
Free eBook: Microsoft Azure Essentials: Azure Machine Learning
http://blogs.msdn.com/b/microsoft_press/archive/2015/04/15/free-ebook-microsoft-azure-
essentials-azure-machine-learning.aspx
Azure Machine Learning для Data Scientist
http://habrahabr.ru/company/microsoft/blog/254637/
Azure Machine Learning: Get started now
http://azure.microsoft.com/uk-ua/services/machine-learning/
Tutorial: Building a classification model in Azure ML
http://gallery.azureml.net/Experiment/01b2765fa75147ce99679e18482d280f