epgy:ai13:day_8

# EPGY Day 8

## Morning

Question: Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what's behind the doors, opens another door, say No. 3, which has a goat. He then says to you, “Do you want to pick door No. 2?” Is it to your advantage to switch your choice?

• 10:00 Tracy Ballinger, UC Santa Cruz
• 11:00 Dave Mark, Intrinsic Algorithm

Cyber Stalking Assignments

• AI Engineer - AI Scientist

Research Clusters - 20 minutes

Red and Black Review

Probability Range

• 0 ≤ P(A) ≤ 1

• P(A∪B) = P(A) + P(B) - P(A∩B)

Disjoint Events Events A and B are disjoint iff

• P(A∩B) = 0

Conditional Probability

• P(A | B) = P(A∩B) / P(B)
True Bayesians actually consider conditional probabilities as more basic than joint probabilities . It is easy to define P(A|B) without reference to the joint probability P(A,B). To see this note that we can rearrange the conditional probability formula to get:
P(A|B) P(B) = P(A,B)
but by symmetry we can also get:
P(B|A) P(A) = P(A,B)
It follows that:
which is the so-called Bayes Rule.

Remember: Finite State Machines? - Deterministic

Building a k-D tree

Application: 3-D graphics

```Algorithm BuildKDTree (S, depth)

input: a set of points S, the current depth depth
output: the root of a kD-tree storing S

if S contains only one point
then return a leaf storing this point
else if depth is even
then Split S into 2 subsets with a vertical line L through the median x-coord
else Split, 2 subsets with a horizontal line

v_left <- BuildKDTree (S1, depth+1)
v_right <- BuildKDTree (S2, depth+1)
v <- L
return v
```

Data Classification

Application: Separating Audio Tracks

Audio ML: http://cs229.weebly.com/index.html

ConceptNet

Application: Language

Language ML:

• 20Q Wordnet Conceptnet
• Argument Champion
• Restaurant Game

Decision Trees

## Afternoon 