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Course 10 : Introduction to machine learning by Christoph Evers

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Course 10 : Introduction to machine learning by Christoph Evers

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For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
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"Data is the new oil" - Many companies and professionals do not know how to use their data or are not aware of the added value they could gain from it.

It is in response to these problems that the project “Brussels: The Beating Heart of Big Data” was born.

This project, financed by the Region of Brussels Capital and organised by Betacowork, offers 3 training cycles of 10 courses on big data, at both beginner and advanced levels. These 3 cycles will be followed by a Hackathon weekend.

No prerequisites are required to start these courses. The aim of these courses is to familiarize participants with the principles of Big Data.
------
For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/

For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
---------
"Data is the new oil" - Many companies and professionals do not know how to use their data or are not aware of the added value they could gain from it.

It is in response to these problems that the project “Brussels: The Beating Heart of Big Data” was born.

This project, financed by the Region of Brussels Capital and organised by Betacowork, offers 3 training cycles of 10 courses on big data, at both beginner and advanced levels. These 3 cycles will be followed by a Hackathon weekend.

No prerequisites are required to start these courses. The aim of these courses is to familiarize participants with the principles of Big Data.
------
For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/

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Course 10 : Introduction to machine learning by Christoph Evers

  1. 1. And why…1 2 3 WHAT IS MACHINE LEARNING A BIT OF HISTORY TYPES OF MACHINE LEARNING Biggest steps in A.I. Global Overview 4 5 ILLUSTRATION OF SOME ALGORITHMS DEEP LEARNING AND NEXT STEP?
  2. 2. Course by Christoph EVERS Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions. Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. Machine learning is a branch of computer science in which you devise or study the design of algorithms that can learn. Optimizing a performance criterion using example data and past experience
  3. 3. Course by Christoph EVERS Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions. Machine learning algorithms can figure out how to perform important tasks by generalizing from examples (data). Machine learning is a branch of computer science in which you devise or study the design of algorithms that can learn. Optimizing a performance criterion using example data and past experience
  4. 4. Course by Christoph EVERS Not the same HYPE as Big Data
  5. 5. Course by Christoph EVERS Computers are far more intelligent (fast) in calculation. Less errors while operating repeatedly. INTUITION FAILS AT HIGH DIMENSION! AI is to automate tasks that people feel are redundant. There is more and more DATA to make it work!
  6. 6. Course by Christoph EVERS Data Growth is promising for Machine Learning
  7. 7. Course by Christoph EVERS BAYES Theorem It defines the probability of an event based on prior knowledge of conditions that might be related to it. Cambridge's EDSAC stored-programs hold their instructions in same memory used for data . Alan Turing "Can machines think?" First Neural Network (M.I.T.) Marvin Minsky and Dean Edmonds built the first artificial neural network – a computer-based simulation of the way organic brains work.
  8. 8. Course by Christoph EVERS First A.I. desillusion ML lost its magic. From lots of enthusiasm in the 50’s & 60’s to diminished funding in the early 70’s… up to the late 80’s DeepBlue beats Garry Kasparov First time the machine beats the best human in a chess game (after failing in 1996). Backpropagation Neural network image recognition DeepMind Neural network that could learn to play video games simply by analyzing the behavior of pixels on a screen. Apache Hadoop Distributed storage and processing of big data
  9. 9. Course by Christoph EVERS
  10. 10. Course by Christoph EVERS
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  12. 12. Course by Christoph EVERS
  13. 13. And why…1 2 3 WHAT IS MACHINE LEARNING A BIT OF HISTORY TYPES OF MACHINE LEARNING Biggest steps in A.I. Global Overview 4 5 ILLUSTRATION OF SOME ALGORITHMS DEEP LEARNING AND NEXT STEP?
  14. 14. Course by Christoph EVERS
  15. 15. Course by Christoph EVERS
  16. 16. Course by Christoph EVERS Classification or Regression (numeric prediction). Needs manual validation part / labels. Learn a function => Looking at several input- output examples.
  17. 17. Course by Christoph EVERS Grouping similar inputs. No manual pre-validation / label. Detecting regularities in input data => Developing patterns (Density Estimation Approach).
  18. 18. Course by Christoph EVERS No teacher / explicit telling. Generally reward based. Interactions with a dynamic environment. Try, make errors and correct.
  19. 19. Course by Christoph EVERS
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  21. 21. Course by Christoph EVERS 1. How much or how many? (regression) 2. Which category? (classification) 3. Which group? (clustering) 4. Is this weird? (anomaly detection) 5. Which option should be taken? (recommendation) TYPICAL QUESTIONS
  22. 22. Course by Christoph EVERS EVALUATION REPRESENTATION OPTIMIZATION
  23. 23. Course by Christoph EVERS EVALUATION Scoring function => Good classification vs bad REPRESENTATION Hypothesis definition => Algorithm choice OPTIMIZATION Which way (algorithm, parameters,…) for the EVALUATION to be the best for us?
  24. 24. Course by Christoph EVERS
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  26. 26. Course by Christoph EVERS BAYES CLASSIFICATION DECISION TREE LINEAR REGRESSION
  27. 27. Course by Christoph EVERS
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  33. 33. Course by Christoph EVERS Ï • “Cheap” => ? • No subject => 67% • Viagra => ...
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  44. 44. Course by Christoph EVERS LOSS FUNCTION : MEASURING ERROR
  45. 45. Course by Christoph EVERS FITTING TRAINING DATA WELL BUT NOT TEST DATA : BIAS-VARIANCE TRADE-OFF NO TEST DATA / BAD TEST DATA NOT PICKING THE RIGHT ALGORITHM…
  46. 46. Course by Christoph EVERS
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  53. 53. Course by Christoph EVERS BIG DATA STACK
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