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In accordance with the Coursera Honor Code, I (Anonymous Student) certify that the answers here are my own work.

Question 1

Suppose you know that

A∨C $A \vee C$ and

B∨~C $B \vee \tilde{}C$ are true. Which of the following is entailed, i.e., follows logically, from these two statements?

A∧B $A \land B$

~A∧~B $\tilde{}A \land \tilde{}B$

A∨B $A \vee B$

~A∨B $\tilde{}A \vee B$

Question 2

The semantic web envisioned .. :

learning topics and sentiment from web-pages and tweets

being able to answer specific questions rather than merely return search results

replacing HTTP with OWL and RDF

faster, personalized search

Question 3

Which of the following do you agree with: (Check all that apply)

the naive Bayes classifier is an example of a Bayesian network.

Bayesian networks allow us to perform adductive reasoning.

logical inference derives new facts from old, using rules.

statements expressed logically must be provably true or false

Question 4

The graphical notation of a Baysian network encodes attempts to encode

our knowledge about conditional and a-priori probabilities

the SQL statements using which inferences can be made

the distinction between what can be measured and what neccesarily needs to be inferred

our understanding about which events might cause others and are therefore dependent

Question 5

Which of the following do you agree with: Check all that apply:

In normal (classical) logic, once we have derived a statement P, we can later derive ~P and revise our conclusions

In Bayesian reasoning, we can estimate our belief that a statement is true with some probability, rather then having to state its truth or falsehood

Bayesian networks provide a mechanism to reason under uncertainty

Normal (classical) logic assumes that a statement can be either true or false

In accordance with the Coursera Honor Code, I (Anonymous Student) certify that the answers here are my own work.

You cannot submit your work until you agree to the Honor Code. Thanks!

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