machine learning, hype or hit?
TRANSCRIPT
ANP126Machine Learning: Hype or Hit?Fred Verheul
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Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
3. Demo, SAP ICN
4. Skill set for aspiring ML experts
5. Take-aways
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Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
3. Demo, SAP ICN
4. Skill set for aspiring ML experts
5. Take-aways
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Machine Learning
"Field of study that gives computers the ability to learnwithout being explicitly programmed” (Arthur Samuel, 1959)
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What is Machine Learning?
Computer
Computer
Traditional Programming
Machine Learning
Data
Data
Program Output
ProgramOutput
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Examples: Recommender systems
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Examples: Natural Language Processing
Siri
Google Translate
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Examples, continued…
SPAM-filtering
Handwriting recognition
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ML in the news: IBM Watson
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ML in the news: Deepmind’s AlphaGo
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ML in the news: business example
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Vendor Platforms…
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Tricking a neural network…
A cat! Surely also a cat?!
More examples and explanation by Julia Evans (@b0rk)
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Machine Learning gone wrong
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Data Mining Fail (by Carina C. Zona)
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Prediction is hard…
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Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
3. Demo, SAP ICN
4. Skill set for aspiring ML experts
5. Take-aways
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CRISP-DM: data mining process
ML important
ML important
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Data: terminology
featuretarget / label
instance
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Examples of ML tasksSupervised learning
Regression target is numeric
Classification target is categorical
Unsupervised learning
Clustering
Dimensionalityreduction
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Exploratory Data Analysis
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Data preparation
• Data Cleaning
• Missing Data
• Feature Engineering• Normalization• Categorical data Numerical features• Log-based features or target• Date/time-related features• Combine features, e.g. by +, -, x, /
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Modeling: so many algorithms…
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ML Algorithms: by RepresentationCollection of candidate models/programs, aka hypothesis space
Decision trees
Instance-based
Neural networks
Model ensembles
ML Algorithms: by Evaluation
Evaluation: Quality measure for a model
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Regression
Example metric: Root Mean Squared Error
RMSE =
Binary classification: confusion matrix
Accuracy: 8 + 971 -> 97,9%
Example: medical test for a disease
Accuracy: Better evaluation metrics:• Precision: 8 / (8 + 19)• Recall: 8 / (8 + 2)
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Optimization: how the algorithm ‘learns’, depends on representation and evaluation
ML Algorithms: by Optimization
Greedy Search, ex. of combinatorial optimization
Gradient Descent (or in general: Convex Optimization)
Linear Programming (or in general:Constrained/Nonlinear Optimization)
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Algorithms by Evaluation: Heuristics
• Hill climbing
• Simulated Annealing
• Nelder-Mead Simplex Method
• Artificial Bee Colony Optimization
• Genetic Algorithms
• Particle Swarm Optimization
• Ant Colony Optimization
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Choice of ML-algorithm, considerations
• Size & Dimensionality of training set
• Computational efficiency
• Model building, no of parameters• Eager vs lazy learning• Online vs batch
• Interpretability
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Evaluation: training vs test data
5-fold cross validation
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Training error vs test error
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Overfitting
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Chebishev distance (L∞-norm: || ||∞ )
|| P – Q ||∞ = max( , )
Number of moves of a King on a chessboard ;-)
Manhattan distance (L1-norm: || ||1 )
|| P – Q ||1 = +
0 1 2 3 4 5 6 7 8 9012345678
Line through (2,2) and (6,5)Line y = 2 (between 2 and 6)Vertical line x = 6 (between 2 and 5)
Distance metrics
Euclidean distance (L2-norm: || ||2 )
|| P – Q ||2 = (length of)
P
Q
Many more: Cosine distance, Edit distance (aka Levenshtein distance), …
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Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
3. Demo, SAP ICN
4. Skill set for aspiring ML experts
5. Take-aways
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Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
3. Demo, SAP ICN
4. Skill set for aspiring ML experts
5. Take-aways
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So you want to be a Data Scientist?
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CRISP-DM: data mining process
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Hacking skills
• Programming languages:
• Libraries (examples):• Tensorflow, Caffe, Theano, Keras• SciPy & scikit-learn• Spark MLLib (Scala/Java/Python)
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More math skills that may be needed…
Calculus Linear Algebra
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Data Science for Business
• Focuses more on general principles than specific algorithms
• Not math-heavy, does contain some math
• O’Reilly link: http://shop.oreilly.com/product/0636920028918.do
• Book website: http://data-science-for-biz.com/DSB/Home.html
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Agenda
1. Introduction: Hype or Hit?!
2. Machine Learning
3. Demo, SAP ICN
4. Skill set for aspiring ML experts
5. Take-aways
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What has NOT been covered
• Deep learning / Neural Networks
• Specifics of ML-algorithms
• Tools / Libraries / Code
• SAP Products, like HANA / Predictive Analytics / Vora / …
• Hardware
• …
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Take-aways
• Goal of ML: generalize from training data (not optimization!!)
• Part of ‘Data Mining Process’, not a goal in and of itself
• No magic! Just some clever algorithms…
• Increasingly important non-technical aspects:• Ethics
• Algorithmic transparency
