Machine learning for the curious but scared

Introductory talk explaining the intuitions behind some basic machine learning concepts.

Transcript

1. MACHINE LEARNING FOR THE CURIOUS BUT SCARED ELLEN KÖNIG @ellen_koenig
2. SO, EXACTLY WHAT DOES IT MEAN WHEN A MACHINE “LEARNS”?
3. ONE WAY TO DEFINE LEARNING: LEARNING FROM EXPERIENCE BEING ABLE TO DEAL WITH NEW SITUATIONS BASED ON THE PAST
4. OF HUMANS AND MACHINES WHAT HAPPENS DURING LEARNING? TRAINING DATA MACHINE LEARNING ALGORITHM MODEL FUNCTION (HYPOTHESIS) Input data about the world Processing by internal resources Learned represen- tation
5. WHAT DOES THAT LOOK LIKE IN PRACTICE EXAMPLES Example Input data Learned Model Self-driving cars Terrain data (slope, roughness, etc.) Function mapping terrain to speed Price prediction engine Customer & market attributes and past prices Function mapping customer and market attributes to prices Gene sequence identification Lots and lots of genome data Clusters of re- occuring gene sequence patterns
6. SO, EXACTLY HOW DOES A MACHINE LEARN?
7. COMPONENTS OF A COMPLETE MACHINE LEARNING SYSTEM WHAT DOES A MACHINE NEED TO LEARN? TRAINING DATA TEST DATA ML ALGORITHM MODEL (HYPOTHESIS) RESULT FEEDBACK
8. TWO BASIC KINDS OF MACHINE LEARNING SUPERVISED VS UNSUPERVISED LEARNING User tastes User 1 likes The Clash User 23 likes Die Ärzte User 42 likes Helene Fischer User 77 likes The Sex Pistols User 99 likes Heino Rain Wind Umbrella? heavy light yes none light no light strong no light light yes none strong no Supervised Unsupervised
9. LINEAR REGRESSION A SIMPLE SUPERVISED LEARNING ALGORITHM
10. K-MEANS A SIMPLE UNSUPERVISED LEARNING ALGORITHM
11. SO, HOW CAN I GET STARTED IN TEACHING A MACHINE TO LEARN?
12. WHERE TO CONTINUE 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) ▸ 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) ▸ 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
13. BONUS A BASIC WORKFLOW FOR WORKING ON MACHINE LEARNING PROBLEMS 1. Understand the problem and context 2. Understand, clean and prepare the data 3. For supervised learning: Split into training and test data 4. Evaluate different algorithms with default parameters 5. Optimize the parameters and compute the results 6. Interpret and present the results
14. LICENCE: CREATIVE COMMONS “ATTRIBUTION - SHARE ALIKE” 4.0 HTTPS:// CREATIVECOMMONS.ORG/LICENSES/BY-SA/4.0/ IMAGE CREDITS ▸ Slide 2: http://www.thebluediamondgallery.com/highlighted/l/learning.html ▸ Slide 3: All https://pixabay.com/ ▸ Slide 4: https://en.wikipedia.org/wiki/Consciousness#/media/ File:Neural_Correlates_Of_Consciousness.jpg ▸ Slide 5: Based on https://commons.wikimedia.org/wiki/ File:Machine_Learning_Technique..JPG ▸ Slide 9: ▸ https://commons.wikimedia.org/w/index.php?curid=11967659 ▸ https://commons.wikimedia.org/wiki/File:Residuals_for_Linear_Regression_Fit.png ▸ Slide 10: https://commons.wikimedia.org/wiki/ File:Kmeans_animation_withoutWatermark.gif