Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014 1 8. Machine Learning Applied Artificial Intelligence Prof. Dr. Bernhard Humm Faculty of Computer Science Hochschule Darmstadt – University of Applied Sciences
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Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014 1
8. Machine Learning Applied Artificial Intelligence Prof. Dr. Bernhard Humm Faculty of Computer Science Hochschule Darmstadt – University of Applied Sciences
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Retrospective Natural Language Processing
• Name and explain different areas of NLP
• What are the “7 levels of language understanding“?
• What is tokenizing, sentence splitting, POS tagging, and parsing?
• What do language resources offer to NLP? Give examples
• What do NLP frameworks offer? Give examples
• What do NLP services offer? Give examples
2
Agenda
• Overview
• ML Applications
• ML Tasks
• ML Approaches
• ML Tools
• Services / Product Map
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014 3
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
What is Machine Learning (ML)?
4
Generating a model based on inputs and using it for making decisions or predictions
( rather than programming instructions explicitly )
Agenda
• Overview
• ML Applications
• ML Tasks
• ML Approaches
• ML Tools
• Services / Product Map
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014 5
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Applications of ML: Spam filtering
• Task: classify new e-mails as spam or not spam
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Spam filter
New e-mails
Automatically classified
Manually classified
Corrections
ML input
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Stock market analysis
• Task: make recommendations on buying and selling stocks
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Prediction
Current stock values
History of stock values
ML input
Recommendation
Decision
Image source: Wikimedia
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Detecting credit card fraud
• Task: Detect fraud in credit card payments
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Fraud detection
CC payments
Automatically classified
Manually classified
Corrections
ML input
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Recommender systems
• Task: Recommending customers suitable products
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Recommender system
Order
Recommendation of related products
ML input
Purchasing behaviour of other customers or customer groups
Agenda
• Overview
• ML Applications
• ML Tasks
• ML Approaches
• ML Tools
• Services / Product Map
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014 10
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Categories of ML tasks
• P.S. Other categorizations / groupings are possible
11
Machine Learning Task
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Classifi-cation
Regression Clustering Feature
selection / extraction
Topic modeling
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Categories of ML tasks
• Given: Example inputs and desired outputs
• Goal: Learn a general rule that maps inputs to outputs
Supervised learning
• Given: Data inputs (e.g., documents)
• Goal: Find structure in the inputs
Unsupervised learning
• Setting: An agent interacts with a dynamic environment in which it must perform a goal
• Goal: Improving the agent‘s behaviour
Reinforcement learning
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Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Supervised learning subcategories
• Given: Training inputs (records) which are divided into two or more classes
• Goal: Produce model to classify new inputs
• Examples: spam filter, fraud detection, …
Classification
• Given: Training data (records) with continuous (not discrete) output values
• Goal: Produce model to predict output values for new inputs
• Example: stock value prediction
Regression
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Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Unsupervised learning subcategories
•Given: Set of input records
•Goal: Identifying clusters (groups of similar records)
•Example: Customer grouping Clustering
•Given: Set of input records with attributes („features“)
•Goal: Find a subset of the original attributes that are equally well suited for classification / clustering tasks
Feature selection / extraction
•Given: Set of text documents
•Goal: Find abstract topics that occur in several documents and classify documents accordingly
Topic modeling
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Agenda
• Overview
• ML Applications
• ML Tasks
• ML Approaches
• ML Tools
• Services / Product Map
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014 15
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Decision Tree Learning
• Used for supervised learning
(classification, regression)
• Training input: Training data
(records) with output values
(discrete or continuous
• Learning result: decision tree that
allows classifying / predicting output
values of new data records
• Example (figure): Decision tree for
classfying passengers on the Titanic
in survived / died
16 Image source: Wikipedia
Prof. Dr. Bernhard Humm, Darmstadt University of Applied Sciences. www.fbi.h-da.de/~b.humm. 18.11.2014
Artificial Neural Networks (ANN)
• Inspired by brain / nervous system:
- Neurons connected via dentrites
- Reduce resistance if fired repeatedly
• Artificial Neuron:
- Weighted inputs
- Function, e.g., weighted sum
- Filter, e.g, threshold output
• Artificial Neural Network (ANN):
- Input layer, output layer, and possibly
intermediate layers of neurons
- Training phase: weights are adjusted via
known cases
- Regognition phase: output is produced for
new cases
17 Source: Ivan Galkin, U. MASS Lowell ( http://ulcar.uml.edu/~iag/CS/Intro-to-ANN.html )