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Machine Learning
Machine learning
Machine learning is an application of
artificial intelligence (AI) that provides
systems the ability to automatically learn
and improve from experience without
being explicitly programmed. Machine
learning focuses on the development of
computer programs that can access data
and use it learn for themselves.
The process of learning begins with
observations or data, such as examples,
direct experience, or instruction, in order
to look for patterns in data and make
better decisions in the future based on the
examples that we provide.
The primary aim is to allow the computers learn automatically
without human intervention or assistance and adjust actions
accordingly.
Some machine learning methods:
Machine learning algorithms are often categorized as supervised or unsupervised.
Types of Machine
Learning
Supervised
Learning
Unsupervis
ed Learning
Re-inforcement
Learning
• Well defined goals
• Reverse Engineering
• Example – Fraud / Non-
Fraud transactions,
Inventory management
• Outcome is based
only on inputs
• Outcome-Typically
clustering or
segmentation
• Start state and end
state are defined
• The agent discovers
the relationships on
its own
•Supervised machine learning algorithms can apply what has been learned in
the past to new data using labeled examples to predict future events. Starting
from the analysis of a known training dataset, the learning algorithm
produces an inferred function to make predictions about the output values.
The system is able to provide targets for any new input after sufficient
training. The learning algorithm can also compare its output with the correct,
intended output and find errors in order to modify the model accordingly.
Machine learning
•In contrast, unsupervised machine learning algorithms are used
when the information used to train is neither classified nor labeled.
Unsupervised learning studies how systems can infer a function to
describe a hidden structure from unlabeled data. The system
doesn’t figure out the right output, but it explores the data and can
draw inferences from datasets to describe hidden structures from
unlabeled data.
Machine learning
•Semi-supervised machine learning algorithms fall somewhere in between
supervised and unsupervised learning, since they use both labeled and
unlabeled data for training – typically a small amount of labeled data and a
large amount of unlabeled data. The systems that use this method are able
to considerably improve learning accuracy. Usually, semi-supervised
learning is chosen when the acquired labeled data requires skilled and
relevant resources in order to train it / learn from it. Otherwise, acquiring
unlabeled data generally doesn’t require additional resources.
•Reinforcement machine learning algorithms is a learning method
that interacts with its environment by producing actions and
discovers errors or rewards. Trial and error search and delayed
reward are the most relevant characteristics of reinforcement
learning. This method allows machines and software agents to
automatically determine the ideal behavior within a specific context
in order to maximize its performance. Simple reward feedback is
required for the agent to learn which action is best; this is known as
the reinforcement signal.
Machine learning enables analysis of massive quantities of data. While it
generally delivers faster, more accurate results in order to identify
profitable opportunities or dangerous risks, it may also require additional
time and resources to train it properly. Combining machine learning with
AI and cognitive technologies can make it even more effective in
processing large volumes of information.
Machine learning
Thanks!
Maedeh Delparish
m.delparish.md@gmail.com

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Machine learning

  • 3. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.
  • 4. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
  • 5. Some machine learning methods: Machine learning algorithms are often categorized as supervised or unsupervised. Types of Machine Learning Supervised Learning Unsupervis ed Learning Re-inforcement Learning • Well defined goals • Reverse Engineering • Example – Fraud / Non- Fraud transactions, Inventory management • Outcome is based only on inputs • Outcome-Typically clustering or segmentation • Start state and end state are defined • The agent discovers the relationships on its own
  • 6. •Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  • 8. •In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
  • 10. •Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
  • 11. •Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
  • 12. Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.