# Social Gaming at TU München

## Flashcards and summaries for Social Gaming at the TU München

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## Study with flashcards and summaries for the course Social Gaming at the TU München

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

M) Explain the basic principles behind a tf-idf (term-frequency/inverse document frequency) representation of a text! (one sentence for tf and one for idf!)

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

14. Why don‘t we have the full joint probability distribution p(X,Z|θ)?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

13. Can You explain the formula for the responsibilities from an intuitive point of view?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

12. What is the meaning of the latent variables Z = {z_nk}?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

11. What is a 1 of K representation?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

9. Why do we take the logarithm of the likelihood? How do we choose the base of the logarithm ? Why?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

8. What does „iid“ mean? What are the consequences here? Can You point them out in an expression on these slides?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

7. What is the paradigm of the „Maximum Likelihood concept“?

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

6. What are advantages of Gaussian Mixture Models compared to Fuzzy C-Means? Informally explain the nature and geometrical interpretation of the GMM quantities μ_k and Σ_k!

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

4. State and explain two advantages of DBSCAN (compared to K-­‐Means)! (1 sentence each)

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

1. Explain briefly the following three characterizations for clustering-approaches / methods:

• exclusive vs. non-exclusive
• c­risp vs. fuzzy
• hierarchical vs. non-­hierarchical

### Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

1. Define „Social Intelligence“ for IT systems! Which parts of your definition apply to the field of Multi Agent Systems and which parts are related to Social Signal Processing?

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## Exemplary flashcards for Social Gaming at the TU München on StudySmarter:

Social Gaming

M) Explain the basic principles behind a tf-idf (term-frequency/inverse document frequency) representation of a text! (one sentence for tf and one for idf!)

tf: Häufigkeit eines Terms in einem Dokument
idf: Bedeutung des Terms in der Gesamtmenge der betrachteten Docs

Social Gaming

14. Why don‘t we have the full joint probability distribution p(X,Z|θ)?

We don’t know the full data set (X,Z).

Social Gaming

13. Can You explain the formula for the responsibilities from an intuitive point of view?

How much is cluster k responsible for point x.

Social Gaming

12. What is the meaning of the latent variables Z = {z_nk}?

Latent variables are specifying the identity of the mixture component of each observation, each distributed according to a K-dimensional categorical distribution.

Social Gaming

11. What is a 1 of K representation?

It is a vector which has an x_i = 1 and all other x ≠ x_i = 0. (There are k different possibilities)

Social Gaming

9. Why do we take the logarithm of the likelihood? How do we choose the base of the logarithm ? Why?

We use the monotonicity of the logarithm to get the maximum of the function (first derivation = 0). This is a lot easier with log and we get the same position.

Social Gaming

8. What does „iid“ mean? What are the consequences here? Can You point them out in an expression on these slides?

iid = independent and identically distributed random variables

Social Gaming

7. What is the paradigm of the „Maximum Likelihood concept“?

parameterabhängige Abschätzung

Social Gaming

6. What are advantages of Gaussian Mixture Models compared to Fuzzy C-Means? Informally explain the nature and geometrical interpretation of the GMM quantities μ_k and Σ_k!

Fuzzy C-Means nimmt eher spherische Cluster an –> Gaussian Mixture Models ist da besser.

Social Gaming

4. State and explain two advantages of DBSCAN (compared to K-­‐Means)! (1 sentence each)
• You don’t need to know K, so easier to compute in this aspect.
• Takes noise into account, which improves clustering quality.

• Can be used for non-spherical clusters, which improves clustering quality.

Social Gaming

1. Explain briefly the following three characterizations for clustering-approaches / methods:
• exclusive vs. non-exclusive
• c­risp vs. fuzzy
• hierarchical vs. non-­hierarchical
• exclusive: nicht überlappende Cluster
• non-exclusive: überlappende
• crisp: ein Element ist im Cluster
• fuzzy: Element ist in mehreren Clustern
• hierarchical: imposes tree structure
• non-hierarchical: doesn’t impose tree structure

Social Gaming

1. Define „Social Intelligence“ for IT systems! Which parts of your definition apply to the field of Multi Agent Systems and which parts are related to Social Signal Processing?

Ability to express and recognize social signals / social behaviors from other human and IT-­agent individuals in order to „function“ in a society with other human and IT-­agent individuals in view of (pareto-­)optimizing own and other IT agent‘s and fellow human‘s utility function (survival, reproduction, …) via cooperation.
green -> Social Signal Processing
blue -> Multi-Agent Systems

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