The document provides an introduction to data science and machine learning, emphasizing the importance of project frameworks and skills necessary for successful implementation. It discusses the relationship between machine learning, artificial intelligence, and data mining, highlighting that machine learning algorithms learn from data without explicit programming. Additionally, it outlines the distinction between statistical modeling and machine learning approaches, particularly in terms of complexity and data size.

1. Introduction to Data Science
Frank Kienle
Machine Learning (Intro)

2. Overall project framework
01/08/2017 p. 2
Overall skills framework
The skills framework gives guidance
about the different domains we have to
group for a successful project
or data science training
The project framework or process model
gives a data science team guidance
how to tackle a problem

3. Terminology embedding
01/08/2017 p. 3
One possible view of the overall embedding in computer science

4. Terminology embedding
01/08/2017 p. 4
Deep Learning
A subset of machine learning
algorithms, composed of
multilayered neural networks capable
to learn on vast amounts of data,
mainly within the domain of speech
and image recognition
Machine learning is the art to
construct a ,task specific’ model
that can learn from one data set and
make predictions on another data
set. Thus it enables computers the
ability to learn without being
explicitly programmed. ML is in
operation within many different
domains and use cases, like fraud
detection, spam classification,
demand forecasts, ….
the term artificial intelligence is
applied when a machine mimics
"cognitive" functions that humans
associate with other human minds
Machine Learning
Artificial Intelligence
AI systems are always composed
of many different components and
techniques to perform learning and
problem solving tasks

5. 01/08/2017 p. 5
Source: http://www.sensorsmag.com/components/artificial-intelligence-autonomous-driving
Artificial Systems are always composed of many
components

6. 01/08/2017 p. 6
https://www.codeproject.com/Articles/1182210/Artificial-Intelligence

7. The "standard interpretation" of
the Turing Test, in which player
C, the interrogator, is given the
task of trying to determine
which player – A or B – is a
computer and which is a
human. The interrogator is
limited to using the responses
to written questions to make
the determination.
Turing Test for artificial intelligence
01/08/2017 p. 7
Juan Alberto Sánchez Margallo -
https://commons.wikimedia.org/wiki/File:Test_de_Turing.jpg

8. Artificial intelligence is …
the term "artificial intelligence" is applied when a machine mimics "cognitive"
functions that humans associate with other human minds, such as "learning" and
"problem solving"
Machine Learning is …
an algorithm that can learn from data without relying on rules-based
programming.
Statistical Modeling is …
formalization of relationships between variables in the form of mathematical
equations.
Machine Learning vs. Statistical Modeling
01/08/2017 Frank Kienle, p. 8

9. Data Mining
• Goal of the data mining process is to extract information from a data set and
transform it into an understandable structure for further use
• Stronger emphasis on volume, variety (e.g. terabytes, )
• Often simple algorithms
Machine Learning approach
• Emphasizes on mathematical description
• Often more sophisticated algorithms (e.g., Support Vector Machines)
• Data sets tend to be smaller compared to data mining problems
In business applications:
the larger the data set, the simpler the mathematical realization to perform the task
no machine learning without data mining before
Data Mining vs. Machine Learning
01/08/2017 p. 9