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MACHINE LEARNING
What do you want to
know?
Question-driven
Big Data
Internet related structured (databases, sensors) and
unstructured (social posts, text messages) available
datasets.
Data analysis
Statistics and logics
Classical logic only permits
conclusions of true or false.
Fuzzy logic evaluate ranges
between 0 and 1 defining degrees of
truth.
Statistics and logics (cont.)
Frequentist: 50% of probability to be full and 50% of probability to be empty.
Bayesian: The probability will be check after estimate probability of to be drunk
regarding drinker mood.
Fuzzy: The glass is full and empty at sametime. With more pertinence for full if the
drinker is in good mood or more pertinence for empty if the drinker is in bad mood.
Bordering values for Classic Math Bordering values for Fuzzy Logic
Conclusion
Fuzzy logic (Engineering)
Aircraft automatic pilot.Car parking assistance.
(Sensors reading)
Fuzzy logic (Artificial Intelligence)
Similar to human thinking and
behavior for decision making.
Virtual game environments,
imprecision and aleatory.
Chatbot messaging,
textual interactions.
Neural networks
Clusters of data are used to understand the relationships and correlation levels to take actions. It's similar
to the way of our cerebral neurons and genetic evolution works, from there the terms: Neuro network
and Genetic algorithms.
It can be used for example to represent the probabilistic relationships between diseases and symptoms. Given
symptoms, the network can be used to compute the probabilities of the presence of various diseases.
This techniques/algorithms are divided in:
Supervised (naive bayes or fuzzy rules e.g.) parameters and answers for training.
Unsupervised (K-means p.e.) non-parametric and/or no right/wrong answers.
Normally you use many mixing subject knowledge and data availability.
Use of self-learning algorithms to predict action through network
informations (bigdata e.g.).
“Against data there’s no arguments”
(math try to predict but only real-time data can capture reality)
Neural networks (Machine Learning)
Neurofuzzy network
(knowledge or datamining)
Easier way to communicate linguistic and math together. Each layer of neurons has specific
functions to capture specialists knowledge and automatic data analysis for learning.
Data mining
Use of statistics to find correlation weight
between variables creating networks and
levels of importance.
Airline industry examples
Machine learning

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Machine learning

  • 2. What do you want to know? Question-driven
  • 3. Big Data Internet related structured (databases, sensors) and unstructured (social posts, text messages) available datasets.
  • 5. Statistics and logics Classical logic only permits conclusions of true or false. Fuzzy logic evaluate ranges between 0 and 1 defining degrees of truth.
  • 6. Statistics and logics (cont.) Frequentist: 50% of probability to be full and 50% of probability to be empty. Bayesian: The probability will be check after estimate probability of to be drunk regarding drinker mood. Fuzzy: The glass is full and empty at sametime. With more pertinence for full if the drinker is in good mood or more pertinence for empty if the drinker is in bad mood. Bordering values for Classic Math Bordering values for Fuzzy Logic Conclusion
  • 7. Fuzzy logic (Engineering) Aircraft automatic pilot.Car parking assistance. (Sensors reading)
  • 8. Fuzzy logic (Artificial Intelligence) Similar to human thinking and behavior for decision making. Virtual game environments, imprecision and aleatory. Chatbot messaging, textual interactions.
  • 9. Neural networks Clusters of data are used to understand the relationships and correlation levels to take actions. It's similar to the way of our cerebral neurons and genetic evolution works, from there the terms: Neuro network and Genetic algorithms. It can be used for example to represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. This techniques/algorithms are divided in: Supervised (naive bayes or fuzzy rules e.g.) parameters and answers for training. Unsupervised (K-means p.e.) non-parametric and/or no right/wrong answers. Normally you use many mixing subject knowledge and data availability.
  • 10. Use of self-learning algorithms to predict action through network informations (bigdata e.g.). “Against data there’s no arguments” (math try to predict but only real-time data can capture reality) Neural networks (Machine Learning)
  • 11. Neurofuzzy network (knowledge or datamining) Easier way to communicate linguistic and math together. Each layer of neurons has specific functions to capture specialists knowledge and automatic data analysis for learning.
  • 12. Data mining Use of statistics to find correlation weight between variables creating networks and levels of importance.