Machine learning: Difference between revisions

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Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. <ref> https://www.sas.com/en_us/insights/analytics/machine-learning.html </ref> This is important to our case study as it allows self-driving cars to learn from it’s environment and mistakes.
Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. <ref> https://www.sas.com/en_us/insights/analytics/machine-learning.html </ref> This is important to our case study as it allows self-driving cars to learn from it’s environment and mistakes.


== How does it work or a deeper look ==
== Terminology ==  
 
Basically the program has some sort of end goal. In our case that is driving to a given destination without crashing, or breaking the law. The program now does multiple runs, and in each run, it changes something. The car might break when it sees a red light for example, this is a good run, as the program has not broken the law. On the other hand if the program breaks the law, and speeds up when it sees a red light, it breaks it’s original goal of not breaking the law. Good runs evolve and ‘reproduce’ (mix the good parts) making better versions of the program, until ideally we have a program that knows fulfills its original purpose. <ref> https://en.wikipedia.org/wiki/Evolutionary_algorithm </ref> <ref>https://en.wikipedia.org/wiki/Neuroevolution </ref>


# [[Behavioural data]]
# [[Cloud delivery models:]]
# [[Infrastructure as a service (IaaS)]]
# [[Platform as a service (PaaS)]]
# [[Software as a service (SaaS)]]
# [[Cloud deployment models]]
# [[Collaborative filtering]]
# [[Content-based filtering]]
# [[Cost function]]
# [[F-measure]]
# [[Hyperparameter]]
# [[K-nearest neighbour (k-NN) algorithm]]
# [[Matrix factorization]]
# [[Mean absolute error (MAE)]]
# [[Overfitting]]
# [[Popularity bias]]
# [[Precision]]
# [[Recall]]
# [[Reinforcement learning]]
# [[Right to anonymity]]
# [[Right to privacy]]
# [[Root-mean-square error (RMSE)]]
# [[Stochastic gradient descent]]
# [[Training data]]
== Examples ==  
== Examples ==  


Revision as of 09:58, 8 June 2022

Case study notes[1]

Introduction[edit]

Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. [2] This is important to our case study as it allows self-driving cars to learn from it’s environment and mistakes.

Terminology[edit]

  1. Behavioural data
  2. Cloud delivery models:
  3. Infrastructure as a service (IaaS)
  4. Platform as a service (PaaS)
  5. Software as a service (SaaS)
  6. Cloud deployment models
  7. Collaborative filtering
  8. Content-based filtering
  9. Cost function
  10. F-measure
  11. Hyperparameter
  12. K-nearest neighbour (k-NN) algorithm
  13. Matrix factorization
  14. Mean absolute error (MAE)
  15. Overfitting
  16. Popularity bias
  17. Precision
  18. Recall
  19. Reinforcement learning
  20. Right to anonymity
  21. Right to privacy
  22. Root-mean-square error (RMSE)
  23. Stochastic gradient descent
  24. Training data

Examples[edit]

An excellent, and I truly mean excellent example is MarI/O, a machine learning program that learns how to play mario, and mario kart.
Super Mario World: https://www.youtube.com/watch?v=qv6UVOQ0F44 Mario Kart: https://www.youtube.com/watch?v=S9Y_I9vY8Qw
[3]


Pictures, diagrams[edit]

Steps-to-Predictive-Modelling.jpg [4]

References[edit]

  1. ↑ http://www.flaticon.com/
  2. ↑ https://www.sas.com/en_us/insights/analytics/machine-learning.html
  3. ↑ https://www.youtube.com/channel/UC8aG3LDTDwNR1UQhSn9uVrw
  4. ↑ https://upxacademy.com/introduction-machine-learning/

Devote time and attention to gaining knowledge of (an academic subject), especially by means of books

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