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The Future of Machine Learning
IDAL; Intelligent Data Analysis Laboratory
Universitat de València
http://idal.uv.es
José D...
3
Future …
Something new every single day …
4
Machine Learning
"A computer program is said to learn from
experience E with respect to some class of
tasks T and perfor...
5
Machine learning project
PROBLEM
(Analysis)
Data
extraction
Data process
Feature
engineering
MODELS Validation
All is co...
6
Feature Engineering
PROBLEM
(Analysis)
Data
extraction
Data process
Feature
engineering
MODELS Validation
Curse of dimen...
The correct
use of inputs
is key for a
successful
ML
application
7
Feature Engineering
FEATURES
Feature
Selection
Feature
...
8
Models
PROBLEM
(Analysis)
Data
extraction
Data process
Feature
engineering
MODELS Validation
No free lunch!
9
Models
Any machine learning model has a certain structure and we
have to choose this (for example, the architecture of a...
10
Example: Deep Learning
Promising models without feature engineering; apparently,
they perform pretty well but …
How man...
11
Example: Azure ML
Many elections
12
Automatic Workflows
Automatic Model Selection
Automatic Tuning
Automatic Representation
Automatic Prediction Strategies
...
13
Future: Automatic
15
Future: Automatic
http://www.automaticstatistician.com/
Future: Just around the corner …
Reinforcement Learning
Supervised Learning Unsupervised Learning
Reinforcement Learning
•...
Reinforcement Learning
EXPERIENCE INTERACTION
ARTIFICIAL LEARNING
MAXIMIZATION OF A CERTAIN OBJECTIVE FUNCTION
POLICY
A pr...
Reinforcement Learning
AGENT
ENVIRONMENT
at
st+1 (after action at)
rt+1
st (before action at)
Long term reward
Action-valu...
Reinforcement Learning: Applicability
- Traditionally, RL has been theoretically studied but until
very recently, practica...
Reinforcement Learning: 

An example (drug prescription)
States: evaluation of the state of the patient
!
Actions: possibl...
21
Conclusions
Two ways have been mentioned:!
1. Automatic election of the parameters in a machine learning project
2. Rei...
The Future of Machine Learning
IDAL; Intelligent Data Analysis Laboratory
Universitat de València
http://idal.uv.es
José D...
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L11. The Future of Machine Learning

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Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015

Published in: Data & Analytics
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L11. The Future of Machine Learning

  1. 1. The Future of Machine Learning IDAL; Intelligent Data Analysis Laboratory Universitat de València http://idal.uv.es José D. Martín Guerrero
  2. 2. 3 Future … Something new every single day …
  3. 3. 4 Machine Learning "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”
  4. 4. 5 Machine learning project PROBLEM (Analysis) Data extraction Data process Feature engineering MODELS Validation All is connected!; feedback is always necessary for the success of the project
  5. 5. 6 Feature Engineering PROBLEM (Analysis) Data extraction Data process Feature engineering MODELS Validation Curse of dimensionality Typical problem in bioinformatics: O(103) features & O(10) samples
  6. 6. The correct use of inputs is key for a successful ML application 7 Feature Engineering FEATURES Feature Selection Feature Extraction Manifolds Models We can select a subset (selection); transform (extraction) or "attack" the model directly (deep learning).
  7. 7. 8 Models PROBLEM (Analysis) Data extraction Data process Feature engineering MODELS Validation No free lunch!
  8. 8. 9 Models Any machine learning model has a certain structure and we have to choose this (for example, the architecture of a neural network). First we have to choose the model that we will use in a given problem. Parameters are obtained by search procedures usually controlled by other parameters we have to choose. Parameters Search Algorithm Structure MODEL
  9. 9. 10 Example: Deep Learning Promising models without feature engineering; apparently, they perform pretty well but … How many layers, how many neurons per layer, which activation function?Inputs Outputs Hidden Layers The most widely used algorithm is the backpropagation after initialization using RBM (Restricted Boltzmann Machines); what adaptation constant must one use?; if we use regularization, how do we weigh that factor?; if we use dropout (to avoid overfitting), what % must we remove?; if we inject noise what is the best value for its energy? Hectic tuning
  10. 10. 11 Example: Azure ML Many elections
  11. 11. 12 Automatic Workflows Automatic Model Selection Automatic Tuning Automatic Representation Automatic Prediction Strategies It would be very nice to have a formal apparatus that gives us some ‘optimal’ way of recognizing unusual phenomena and inventing new classes of hypotheses that are most likely to contain the true one; but this remains an art for the creative human mind.” E. T. Jaynes 1985 Future: Automatic
  12. 12. 13 Future: Automatic
  13. 13. 15 Future: Automatic http://www.automaticstatistician.com/
  14. 14. Future: Just around the corner … Reinforcement Learning Supervised Learning Unsupervised Learning Reinforcement Learning • It does not need a teacher to learn a desired signal • There is a goal (objective function) to be maximized • The outcome is a sequence of actions rather than a static model • It can deal with long-term objectives, not only a certain steps ahead in the future!! •  Similar to some stages of human learning
  15. 15. Reinforcement Learning EXPERIENCE INTERACTION ARTIFICIAL LEARNING MAXIMIZATION OF A CERTAIN OBJECTIVE FUNCTION POLICY A priori knowledge Environment adaptation
  16. 16. Reinforcement Learning AGENT ENVIRONMENT at st+1 (after action at) rt+1 st (before action at) Long term reward Action-value function Optimal policy st: State (at time t) at: Action (at time t) rt+1: Immediate reward Discount rate: a reward received k time steps in the future is worth only k−1 times what it would be worth if it were received immediately Values of the discount rate close to 1 avoids the agent to be myopic (maximization of rt+1)
  17. 17. Reinforcement Learning: Applicability - Traditionally, RL has been theoretically studied but until very recently, practical applications were restricted to well-known synthetic problems and/or Robotics. ! ! - Any dynamic problem that can be defined in a state- space, in which certain actions can be taken, and an objective function has to be maximized, is susceptible to be tackled using RL. ! ! - Some practical applications on Marketing or Medicine (individualization of campaigns or treatments). ! ! !
  18. 18. Reinforcement Learning: An example (drug prescription) States: evaluation of the state of the patient ! Actions: possible actions that can taken by doctors wrt to drug prescription ! Reward: the action involves a change in the state. Depending on this resulting state, a reward can be assigned ! The aim is to maximize the long-term reward It is possible to know the dosage (actions) that should be administered to maintain patients within a given state. ! Other factors can also be included in the computation of the reward (e.g., expenses).
  19. 19. 21 Conclusions Two ways have been mentioned:! 1. Automatic election of the parameters in a machine learning project 2. Reinforcement Learning Predicting the future is too challenging to talk about it but it is so exciting that one must talk about it There’s plenty of room to come up with new ideas … already present! 1. Validation in Bayesian nets 2. Quantum Machine Learning
  20. 20. The Future of Machine Learning IDAL; Intelligent Data Analysis Laboratory Universitat de València http://idal.uv.es José D. Martín Guerrero

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