Stochastic Optimization Learning at Universität Graz | Flashcards & Summaries

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# Lernmaterialien für Stochastic Optimization Learning an der Universität Graz

Greife auf kostenlose Karteikarten, Zusammenfassungen, Übungsaufgaben und Altklausuren für deinen Stochastic Optimization Learning Kurs an der Universität Graz zu.

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Characteristics APD
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Replace true function with statistical approximation
Move foreward in time
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Supervised Learninh, Unsupervised Learning, Reinforcement Learning
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Training by correctly labeled data
UL: data driven (clustering)
no solution provided, algorithm finds pattern
RL: decision process, algorithm learns to take actions to macimize reward
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Problems involving many states and actions
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For small number of states and actions: lookup table
But not realistic - use functions
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Learning dimensions
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- Model free or model based (model of rewards or transition properties)
- real world or simulator
- active or passive learning (policy given?
- on policy or off policy

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Optimal Learning
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Exploration vs Exploitation
Best long term strategy may involve sacrifice
Optimal: policy with least number of measurements or lowest sacrifice

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Elements of a learning problem
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1. how to make measurement?
2. effect of measurement?
3. evaluate result of measurement?
4. offline or online learning?
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Nature of measurement decision
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- 0/1: stoppung problems
- Z: discrete set if alternatives (ranking and selection)
- R: continuous set (temperature, speed)
- 0/1/0/0/1/0/1: subset selection
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Effect of measurement
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- Frequentist point of view
- Baysian point of view:
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Policies
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-Deterministic
- Sequential optimal: Dynamic programming
-Sequential: next measurement depends on knowledge state
— exploration
— exploitation
— epsilon greedy
— interval estimation
— boltzman exploration
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Choose measurement that would improve best mean the most
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Properties of KG
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- optimal decision with one measurement remaining

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Exploration vs Exploitation
Value function approximation
Updating Vtn
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• 118625 Karteikarten
• 2140 Studierende
• 78 Lernmaterialien

## Beispielhafte Karteikarten für deinen Stochastic Optimization Learning Kurs an der Universität Graz - von Kommilitonen auf StudySmarter erstellt!

Q:
Characteristics APD
A:
Replace true function with statistical approximation
Move foreward in time
Q:
Supervised Learninh, Unsupervised Learning, Reinforcement Learning
A:
Training by correctly labeled data
UL: data driven (clustering)
no solution provided, algorithm finds pattern
RL: decision process, algorithm learns to take actions to macimize reward
Q:
Problems involving many states and actions
A:
For small number of states and actions: lookup table
But not realistic - use functions
Q:
Learning dimensions
A:
- Model free or model based (model of rewards or transition properties)
- real world or simulator
- active or passive learning (policy given?
- on policy or off policy

Q:
Optimal Learning
A:
Exploration vs Exploitation
Best long term strategy may involve sacrifice
Optimal: policy with least number of measurements or lowest sacrifice

Q:
Elements of a learning problem
A:
1. how to make measurement?
2. effect of measurement?
3. evaluate result of measurement?
4. offline or online learning?
Q:
Nature of measurement decision
A:
- 0/1: stoppung problems
- Z: discrete set if alternatives (ranking and selection)
- R: continuous set (temperature, speed)
- 0/1/0/0/1/0/1: subset selection
Q:
Effect of measurement
A:
- Frequentist point of view
- Baysian point of view:
Q:
Policies
A:
-Deterministic
- Sequential optimal: Dynamic programming
-Sequential: next measurement depends on knowledge state
— exploration
— exploitation
— epsilon greedy
— interval estimation
— boltzman exploration
Q:
A:
Choose measurement that would improve best mean the most
Q:
Properties of KG
A:
- optimal decision with one measurement remaining

Q:
A:
Exploration vs Exploitation
Value function approximation
Updating Vtn

### Erstelle und finde Lernmaterialien auf StudySmarter.

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