Le Machine Learning de A à Z
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Le Machine Learning, l’IA, le DeepLearning, les Statistiques, le Data Mining… bref, tous ces mots sont les buzz words du moment mais que se cache-t-il derrière ? A travers des exemples concrets, on parcourra les différentes approches du Machine Learning, les grandes familles d’algorithmes (n’ayez crainte : sans rentrer dans le cœur de leurs implémentations), puis les outils et les frameworks à la disposition des Data Scientists… et pour finir, on essayera de prédire l’avenir ! Salon Data - Nantes - 19 Septembre 2017 https://salondata.fr/2017/07/12/0930-1030-ml/
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Le Machine Learning de A à Z
- 1. Machine Learning de A à Z NANTES DIGITAL WEEK 19 Septembre 2017
- 2. Alexia Audevart Data & Enthusiasm @aaudevart Présidente meet-up Toulouse Data Science Co-organisatrice du devfest Toulouse
- 3. Definitions Machine Learning Approaches Machine Learning Methods Machine Learning Trends Machine Learning Tools and Frameworks 1 2 3 4 5 Machine Learning Summary
- 4. Types of Data WHAT IS A DATA? • Datum : an item of factual information derived from measurement or research • Data : a collection of facts from which conclusions may be drawn (plural of Datum) 1 Structured Data Semi Structured Data Unstructured Data LOG
- 6. MACHINE LEARNING 1 Experience Task Performance Input Data • Housing Prices • Images • Customer Transactions • Clickstream data Task • Predict Prices • Categorize images • Segment customers • Optimize user flows Performance • Accurate Prices • Correctly sorted images • Coherent groupings • KPI lifts
- 7. MACHINE LEARNING A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. 1 Tom Mitchell – 1998 Experience Task Performance
- 8. QUIZZ 1 Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. Identify the task T, the performance measure P and the experience E • Classifying emails as spam or not spam. • Watching you label emails as spam or not spam. • The number (or fraction) of emails correctly classified as spam/not spam.
- 9. MACHINE LEARNING : WHEN ? Human expertise does not exist Humans are unable to explain their expertise Navigating on Mars Solution changes in time Solution needs to be adapted to particular cases Navigating on Mars Speech Recognition Routing on a computer network User Biometrics 1
- 10. WHAT IS A FEATURE ? Features are elements or dimensions of your dataset. 1
- 11. WHAT IS A MODEL ? Definition : a mathematical representation of relationships in a dataset. 1
- 12. MACHINE LEARNING ALGORITHMS • Learning Algorithm : infer the model parameters from the data • Inference Algorithm : infer prediction from a model 1
- 13. Definitions Machine Learning Approaches Machine Learning Methods Machine Learning Trends Machine Learning Tools and Frameworks 1 2 3 4 5 Machine Learning Summary
- 14. SUPERVISED LEARNING DEFINITION • The correct class of the training data are known. • All data are labeled • The algorithm learn to predict the output from the input data. TWO MAIN GOALS : 2 Predictive Informative Make prediction for a new sample described by its attributes Help to understand the relationship between the inputs and the output.
- 15. SUPERVISED LEARNING 2 # Bedrooms = A Size = B Zip code = C … Price = Y 3 2104 31500 400 000 3 1600 87220 330 000 3 2400 45760 369 000 … … 4 3000 76540 540 000 INPUTS OUTPUT Feature A 𝑌" = ℎ(𝐴, 𝐵, 𝐶, … . ) Model Hypothesis Supervised Learning Goal: Minimize error between Y et 𝑌" Labeled Dataset
- 17. Classification problem • Predict class from observations • Symbolic output SUPERVISED LEARNING 2 Regression problem • Predict value from observations • Numerical output
- 18. SUPERVISED LEARNING - EXAMPLES Spam Email Detection Biomedical domain : differentiation of diseases Speech recognition Time series prediction Handwritten character recognition Object recognition in computer vision prediction electricity load, network usage, stock market prices 2
- 19. UNSUPERVISED LEARNING DEFINITION • The correct class of the training data are NOT known. • All data is unlabeled • The algorithms learn to inherent structure from the input data. Unsupervised learning problems can be further grouped into : 2 Clustering Association Dimension reduction Anomaly Detection
- 21. UNSUPERVISED LEARNING - EXAMPLES Customer Segmentation Face Image Recognition Recommendation system 2
- 22. REINFORCEMENT LEARNING Principles: • Close to human learning • Algorithm learns a policy of how to act in a given environment • Every Action has some impact in the environment, and the environment provides rewards that guides the learning algorithm Goal: • Maximize the expected sum of future rewards 2 Environment: • Stochastic (Tetris) • Adversarial (Chess) • Partially unknown (Bicycle) • Partially observable (Robot)
- 23. Robotics AlphaGoGame Playing REINFORCEMENT LEARNING EXAMPLES 2
- 24. SEMI-SUPERVISED LEARNING • Learning from both labeled and unlabeled data • Using a mixture of supervised and unsupervised techniques 2
- 25. Definitions Machine Learning Approaches Machine Learning Methods Machine Learning Trends Machine Learning Tools and Frameworks 1 2 3 4 5 Machine Learning Summary
- 29. Definitions Machine Learning Approaches Machine Learning Methods Machine Learning Trends Machine Learning Tools and Frameworks 1 2 3 4 5 Machine Learning Summary
- 30. Decisions tree 4 SYMBOLISTS Addition 1 + 1 ? Substraction 1 + ? = 2 Deduction Socrates is Human + Humans are mortal ? Induction Socrates is Human + ? = Socrates is Mortal Origins Logic, Philosophy _______________________ Master Algorithms Inverse Deduction _______________________ Problems Knowledge Composition
- 31. BAYESIANS Statistics Bayesians Origins Statistics _____________________________ Master Algorithms Probabilistic Inference _____________________________ Problems Uncertainty 4
- 32. CONNECTIONISTS A neuronOrigins Neuroscience _____________________________ Master Algorithms Backpropagation _____________________________ Problems Credit Assignment An artificial neuron 4
- 33. EVOLUTIONARIES Genetic Algorithms Applying the idea of genomes and DNA in the evolutionary process to data Origins Evolutionary biology _____________________________ Master Algorithms Genetic Programming _____________________________ Problems Structure Discovery 4
- 34. ANALOGIZERS Nearest NeighborOrigins Psychology _____________________________ Master Algorithms Kernel Machines (SVM : Support Vector Machines) _____________________________ Problems Similarities 4
- 35. EvolutionariesConnectionistsBayesiansSymbolists THE 5 TRIBES OF MACHINE LEARNING Systematically reduce uncertainty Simulate evolutionEmulate the brain Fill the gaps in existing knowledge Source Image : Blog PWC Pedro Domingo-The Master Algorithm 4 Analogizers Notice similarities between old and new Each tribe represents a different school of thought within the machine learning space. They are often combined to attack problems from different angles.
- 36. Definitions Machine Learning Approaches Machine Learning Methods Machine Learning Trends Machine Learning Tools and Frameworks 1 2 3 4 5 Machine Learning Summary
- 37. MACHINE LEARNING 5 Python R Matlab Octave
- 38. Dataiku Knime RapidMiner SAS ML PLATFORMS 5
- 39. Amazon Machine Learning Azure Machine Learning Studio Big ML Google Machine Learning ML IN THE CLOUD 5
- 40. Mahout Spark Machine Learning H20 SCALABLE MACHINE LEARNING 5
- 41. DEEP LEARNING 5 Caffe CNTK TensorFlow Theano MXNet Torch Apache Singa
- 42. What’s next ? Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines Yann LeCun – Director of AI Research, Facebook
Notas do Editor
- statistiques : ensemble de méthodes permettant de décrire et d’analyser des observations (ou des données) data mining permet de découvrir des structures dans de vastes ensembles de données (patterns). Le machine learning : science de la prédiction donne la possibilité à des machines d’apprendre sans être explicitement programmées pour. L’intelligence artificielle définit des comportements et des raisonnements. Deep Learning is a subfield of machine learning where concepts are learned hierarchically. The simplest concepts emerge first, followed by more complicated concepts that build on the simpler ones. Usually, this leads to a simple layered hierarchy of concepts.
- En étude du comportement du consommateur internaute, le clickstream représente l'analyse des chemins empruntés par lui sur un site Internet
- Generalisation Models get complicated Models aren’t magic
- Supervised learning is a way of teaching an algorithm how to do its job when you already have a set of data for which you know “the answer.” Predictive: Make prediction for a new sample described by its attributes Informative: Help to understand the relationship between the inputs and the output.
- Classification A sub-category of Supervised Learning, Classification is the process of taking some sort of input and assigning a label to it. Classification systems are usually used when predictions are of a discrete, or “yes or no” nature. Example: Mapping a picture of someone to a male or female classification. Regression Another sub-category of supervised learning used when the value being predicted differs to a “yes or no” label as it falls somewhere on a continuous spectrum. Regression systems could be used, for example, to answer questions of “How much?” or “How many?”.
- Unsupervised learning is how an algorithm or system analyzes data that isn’t labeled with an answer, then identifies patterns or correlations. Clustering: group observations into « meaningful » groups Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. Dimension reduction Anomaly detection
- Train 60% => fit the parameter Val 20% for CV => tune the parameter Test 20% => evaluate the performance
- Deduction is going from general rules to specific facts Induction is the opposite From specific facts to general rules Symbolists : people who believes in discovering new knowledge by filling in the gaps in the knowledge - Origins : Logic, philosophy Use symbols, rules and logic to represent knowledge and draw logical inference Pb : Knowledge composition Inverse dedeuction / Rules & Decision Trees
- Bayesians : Origins : Statistics Assess the likelihood of occurrence for probabilistic inference Pb : Uncertainty Probabilistic Inference – Naives Bayes or Markov
- Analogizers : have influence from a lot of different fields (the most important is psychology)
- Apache Singa : https://singa.incubator.apache.org/en/docs.html Caffee (C++, developped originally for machine vision projects, CUDA acceleration support CNTK (Computational Network Toolkit) => more optimal speech recognition Mxnet => open source et chois par AWS TensorFlow & Theano : très similaire / syntaxe proche de numpy Torch : utilisé par Facebook AI research (developped in LUA)