Introduction to Data Science

Frank Kienle Machine Learning (Intro)

Overall project framework

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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

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One possible view of the overall embedding in computer science

Terminology embedding

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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

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Source: http://www.sensorsmag.com/components/artificial-intelligence-autonomous-driving

Artificial Systems are always composed of many components

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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

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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

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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

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