Deep convolutional networks, overfitting, RBF kernels, GANs, nonlinear transformations, stochastic gradient descent, and of course the coming singularity and super intelligence. Machine learning buzzwords are all the rage now, but what does it mean for a machine to actually learn? This talk covers the things that are usually assumed as a prerequisite in "introductory" ML courses and tutorials. It describes what happens during learning, how we can represent experience and learnings in a machine-friendly way, and how different types of ML systems work at a high level.

1. MACHINE LEARNING FOR THE
CURIOUS BUT CONFUSED
ELLEN KÖNIG // @ELLEN_KOENIG
Senior Data Scientist @ Native Instruments

2. OVERWHELMED?
BUZZWORD BINGO!

3. SO, EXACTLY WHAT DOES IT
MEAN WHEN A MACHINE
„LEARNS“?

4. WHAT IS LEARNING?
BEING ABLE TO DEAL WITH NEW SITUATIONS BASED ON THE PAST

5. EXAMPLES
WHAT CAN YOU DO WITH MACHINE LEARNING?
Self-driving cars
Price prediction
Gene sequence identification

6. OF HUMANS AND MACHINES
WHAT HAPPENS DURING LEARNING?
DATA
MACHINE
LEARNING
ALGORITHM
MODEL
FUNCTION
Input about
the world
Processing
resources
Learned representation
„DOG“
Neural associationEyes + brainOutside world

7. 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

8. 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) = ?

9. 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

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

12. 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

13. THE 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 1 likes The Sex Pistols
User 42 likes Heino
Rain Wind Umbrella?
heavy light yes
none light no
light strong no
light light yes
none strong no
Supervised Unsupervised

14. LINEAR REGRESSION
A SIMPLE SUPERVISED LEARNING ALGORITHM
Fitting a line: https://towardsdatascience.com/linear-regression-using-least-squares-a4c3456e8570
PRICE IN
10,000 €
SIZE IN SQ. METERS
Apartment Price Prediction

15. K-MEANS
A SIMPLE UNSUPERVISED LEARNING ALGORITHM
Car Model
Clustering
WEIGHT
SPEED

16. 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 and training data

17. 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
▸ More advanced Tutorial based on the same dataset using Python (Scikit-learn, Pandas, Tensorflow): https://blog.socialcops.com/
technology/data-science/machine-learning-python/
▸ 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)

18. BONUS: SO, HOW CAN I GET
STARTED IN TEACHING A
MACHINE TO LEARN?

19. THE STARTING POINT
A BASIC WORKFLOW FOR WORKING ON MACHINE LEARNING PROBLEMS
1. Understand the problem and context
2. Understand & clean the data, create some features
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 the results
7. Repeat with different features until you get useful results

20. THE STARTING POINT
MORE ART THAN SCIENCE

21. LEARN BY REPEATING THE WORKFLOW
RINSE AND REPEAT
PICK ONE TOOL
TRY THE WORKFLOW
PICK A (“TOY”) PROBLEM
PICK A TYPE OF ALGORITHM

22. LICENSE: CREATIVE COMMONS “ATTRIBUTION - SHARE ALIKE” 4.0 HTTPS://
CREATIVECOMMONS.ORG/LICENSES/BY-SA/4.0/
IMAGE CREDITS
▸ Slide 1 : http://work.caltech.edu/dex1.html at 5:20 of the video
▸ Slide 3 & 18: http://www.thebluediamondgallery.com/highlighted/l/learning.html
▸ Slide 3: All https://pixabay.com/
▸ Slide 4 & 8:
▸ 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
▸ Slide 13:
▸ https://commons.wikimedia.org/w/index.php?curid=11967659
▸ https://commons.wikimedia.org/wiki/File:Residuals_for_Linear_Regression_Fit.png
▸ Slide 14: Based on https://commons.wikimedia.org/wiki/File:Kmeans_animation_withoutWatermark.gif
▸ Slide 20: https.//pixabay.com