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In 1959, Arthur Samuel, a computer scientist who pioneered the study of artificial intelligence, described the
machine learning – ML- as «the study that gives computers the ability to learn
without being explicitly programmed. Alan Turing’s seminal article (Turing, 1950) introduced a
reference standard to demonstrate the intelligence of machines, such that a machine has
to be smart and respond in a way that cannot be differentiated from that of a human being.
Machine learning is an application of artificial intelligence in which a computer /
machine learns from past experiences (input data) and makes future predictions. The
The performance of such a system should at least be human level.
In this material, we will focus on clustering problems for non-machine learning.
supervised with the K-Means algorithm. For supervised machine learning we will describe the
classification problem with a proof of the design tree algorithm and the regression algorithm
with an example of linear regression. Below is a summary representing the types
machine learning and some algorithms as examples in the following figure:
Learning objectives:
• Understand the fundamentals of
Artificial Intelligence and Learning
• Describe the learning methods
automatic: supervised and unsupervised
• Use Data Analysis to take
• Understand the limits of algorithms
• Understand and understand programming
in Python, mathematical knowledge
essentials in AI and basic methods of
Target audiences:
Anyone interested in expanding
their knowledge in Artificial Intelligence and
Machine learning
• Engineers, analysts, marketing directors
• Data analysts, data scientists,
data managers
• Anyone interested in techniques of
data mining and machine learning
There are no formal prerequisites for this