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
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 specificâ 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, âŠ.
the term artificial intelligence is
applied when a machine mimics
"cognitive" functions that humans
associate with other human minds
Machine Learning
ArtiïŹcial 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
ArtiïŹcial Systems are always composed of many
components
7. The "standard interpretation" of
the Turing Test, in which player
C, the interrogator, is given the
task of trying to determine
which player â A or B â 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 artiïŹcial intelligence
01/08/2017 p. 7
Juan Alberto SĂĄnchez Margallo -Â
https://commons.wikimedia.org/wiki/File:Test_de_Turing.jpg
8. ArtiïŹcial intelligence is âŠ
the term "artiïŹcial 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 âŠ
an algorithm that can learn from data without relying on rules-based
programming.
Statistical Modeling is âŠ
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
âąâŻ Goal of the data mining process is to extract information from a data set and
transform it into an understandable structure for further use
âąâŻ Stronger emphasis on volume, variety (e.g. terabytes, )
âąâŻ Often simple algorithms
Machine Learning approach
âąâŻ Emphasizes on mathematical description
âąâŻ Often more sophisticated algorithms (e.g., Support Vector Machines)
âąâŻ 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