Machine learning for the curious but scared

MACHINE LEARNING FOR THE CURIOUS BUT SCARED

ELLEN KÖNIG

@ellen_koenig

SO, EXACTLY WHAT DOES IT MEAN WHEN A MACHINE

“LEARNS”?

ONE WAY TO DEFINE LEARNING: LEARNING FROM EXPERIENCE

BEING ABLE TO DEAL WITH NEW SITUATIONS BASED ON THE PAST

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

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

SO, EXACTLY HOW DOES A MACHINE LEARN?

COMPONENTS OF A COMPLETE MACHINE LEARNING SYSTEM

WHAT DOES A MACHINE NEED TO LEARN?

TRAINING DATA

TEST DATA

ML ALGORITHM

MODEL (HYPOTHESIS) RESULT

FEEDBACK

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

LINEAR REGRESSION

A SIMPLE SUPERVISED LEARNING ALGORITHM

K-MEANS

A SIMPLE UNSUPERVISED LEARNING ALGORITHM

SO, HOW CAN I GET STARTED IN TEACHING A

MACHINE TO LEARN?

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

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

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