Upgrade to Pro — share decks privately, control downloads, hide ads and more …

EnthusiastiCon - Machine learning for the curious but scared

EnthusiastiCon - Machine learning for the curious but scared

B27e5bc114b24f86625025d4dae10184?s=128

ellenkoenig

March 31, 2018
Tweet

Transcript

  1. MACHINE LEARNING FOR THE CURIOUS BUT SCARED ELLEN KÖNIG @ellen_koenig

  2. OVERWHELMED? BUZZWORD BINGO!

  3. WHAT IS LEARNING? BEING ABLE TO DEAL WITH NEW SITUATIONS

    BASED ON THE PAST
  4. EXAMPLES WHAT CAN YOU DO WITH MACHINE LEARNING? Self-driving cars

    Price prediction Gene sequence identification
  5. OF HUMANS AND MACHINES WHAT HAPPENS DURING LEARNING? DATA MACHINE

    LEARNING ALGORITHM MODEL FUNCTION Input about the world Processing resources Learned representation „DOG“ Neural association Eyes + brain Outside world
  6. MACHINE FRIENDLY REPRESENTATIONS OF EXPERIENCE HOW DO WE PUT THE

    OUTSIDE WORLD INTO A MACHINE? Input about the world 1 person, 2 trees, 1 animal, lots of grass, 1 path Different grayscale pixels Extracted relevant information People Trees Animals Grass Paths 1 2 1 Yes 1 Numerical representation ( 12 1 1 1 ) Data vector representation Describe or capture Remove context Summarize with numbers
  7. MACHINE FRIENDLY REPRESENTATIONS OF LEARNINGS SO WHAT EXACTLY DO MACHINES

    LEARN? A function is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to exactly one output. (Wikipedia) f ( ) = 1 MACHINES LEARN PREVIOUSLY UNKNOWN FUNCTIONS MAPPING FROM GIVEN INPUT TO GIVEN RESULTS MODEL FUNCTION f ( ) = 0 f (x) = ?
  8. WHAT DOES THAT LOOK LIKE IN PRACTICE? EXAMPLES Example Input

    data Learned Model Terrain data (slope, roughness, etc.) Function mapping terrain to speed Customer & market data and past prices Function mapping input to future prices Gene sequence identificatio Lots and lots of genome data Clusters of re-occuring gene sequence patterns
  9. COMPONENTS OF A COMPLETE MACHINE LEARNING SYSTEM WHAT DOES A

    MACHINE NEED TO LEARN? INPUT DATA ML ALGORITHM MODEL FUNCTION RESULT Unsupervised Learning
  10. COMPONENTS OF A COMPLETE MACHINE LEARNING SYSTEM WHAT DOES A

    MACHINE NEED TO LEARN? TRAINING DATA INPUT DATA ML ALGORITHM MODEL FUNCTION RESULT Supervised Learning
  11. COMPONENTS OF A COMPLETE MACHINE LEARNING SYSTEM WHAT DOES A

    MACHINE NEED TO LEARN? TRAINING DATA INPUT DATA ML ALGORITHM MODEL FUNCTION RESULT FEEDBACK Reinforcement Learning
  12. SUMMARY CORE INTUITIONS FOR MACHINE LEARNING ▸ Machine learning works

    in a very similar way to human learning! ▸ Learning: Pattern recognition, dealing with unfamiliar situations based on experience ▸ Situations and experience can be abstracted into data to be accessible to machines ▸ Machines learn previously unknown functions from data ▸ A ML system consists of input data, ML algorithms, model functions, results and optionally feedback
  13. WHERE TO GET STARTED RECOMMENDED RESOURCES FOR BEGINNERS (IN ORDER

    OF RECOMMENDATION) ▸ Tutorial for the “Kaggle Titanic Competition” (using R): http://trevorstephens.com/post/72916401642/titanic-getting- started-with-r ▸ Online courses (MOOCs): ▸ Udacity: Intro to Machine Learning: https://www.udacity.com/course/intro-to-machine-learning--ud120 (Excellent intro to applied ML using sci-kit learn and Python) ▸ Coursera: Machine Learning: https://www.coursera.org/learn/machine-learning (Friendly intro to the theory behind common ML algorithm) ▸ Machine Learning Mastery: Lots of self-study guides for ML learners http://machinelearningmastery.com/ ▸ UCI ML Repository: Collection of “Toy problems” for ML http://archive.ics.uci.edu/ml/datasets.html ▸ Toolkits: ▸ Scikit-Learn (Python, great online documentation): http://scikit-learn.org/stable/ ▸ stats package (many simple ML algorithms), pre-installed (R) Examples: http://www.statmethods.net/stats/ regression.html ▸ Book: Abu-Mostafa, Magdon-Ismail, Lin: Learning From Data - A Short Course (AMLbook.com ) (Good intro to more academic perspectives, notation and vocabulary on ML)
  14. LICENCE: CREATIVE COMMONS “ATTRIBUTION - SHARE ALIKE” 4.0 HTTPS:// CREATIVECOMMONS.ORG/LICENSES/BY-SA/4.0/

    IMAGE CREDITS ▸ Slide 3: All https://pixabay.com/ ▸ Slide 4: ▸ https://commons.wikimedia.org/wiki/File:Human_genome.png ▸ https://commons.wikimedia.org/wiki/File:Hubbert_US_Lower_48_Gas_Prediction_-_1962.png ▸ https://commons.wikimedia.org/wiki/File:Waymo_self-driving_car_front_view.gk.jpg ▸ Slides 5 -7: https://en.wikipedia.org/wiki/Consciousness#/media/ File:Neural_Correlates_Of_Consciousness.jpg ▸ Slide 7: pixabay.com ▸ Slide 9-11: Based on https://commons.wikimedia.org/wiki/ File:Machine_Learning_Technique..JPG