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Introduction to Data Science
Frank Kienle Machine Learning (Intro)
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
Terminology embedding
01/08/2017 p. 3
One possible view of the overall embedding in computer science
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
01/08/2017 p. 5
Source: http://www.sensorsmag.com/components/artificial-intelligence-autonomous-driving
Artificial Systems are always composed of many components
01/08/2017 p. 6
https://www.codeproject.com/Articles/1182210/Artificial-Intelligence
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
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
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