Term
What is the bayesian formula? 

Definition


Term

Definition
p(AB) = p(A and B) / p(B) 


Term
What does p(A and B) mean? 

Definition
p(A and B) = p(AB) * p(B) 


Term

Definition
p(BA) = p(B and A) / p(A)
= p(AB) * p(B) / p(A) 


Term
Show a joint distribution of 3 variables 

Definition


Term
How can you reduce (the variables of) a joint distribution table? 

Definition
If a variable is independent there is no need to compare it to each of the other variables as if A is independent of B, then p(AB) is just equal to p(A) 


Term
What is a bayesian network used for? 

Definition
 Describe which variables influence which otehr variables
 No connection between two variables implies conditional independence



Term

Definition


Term

Definition
Decision support tool that uses tree like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, utility, etc. 


Term

Definition
The measure of the amount of disorder or surprise in a system
High entropy means we have no idea what is going to happen.
Low entropy means we've pinned thiings down to some extent; we've got some information about what is likely to happen 


Term
What is the entropy formula? 

Definition
H(X) = E_{x}(I(x)) = sum(p(x)log_{2}(p(x)) for all x 


Term
How to build a decision tree? 

Definition
Split the tree on the attribute with the highest information gain. Then recurse 


Term
What is the kneighbour algorithm? 

Definition
http://www.youtube.com/watch?v=4ObVzTuFivY
 It takes a specific point, and classifies it according to the majority vote of the k nearest points



Term
What is supervised learning? 

Definition
 The learner must learn to classify cases but a labelled training set spells out what the right answer should be in each case.
 Knearest neighbour, decision trees, neural nets are all examples of supervised learning



Term
What is reinforcement learning? 

Definition
 Agent lives in an environment, it must choose actions within that world and periodically it gets either positive or negative reinforcement



Term
What is the temporal difference learning equation? 

Definition
V_{i} = V_{i} + a [r + V_{j}  V_{i}]
Where
 Vi = new opinion
 Vj = old opinion
 a = learning rate
 r = actual reward



Term
What is discounting future rewards and how can it be used? 

Definition
Rewards that can be obtained now are usually better than the one we obtain later
We need to discount the future rewards implicit in Vj with a new d term (e.g. d= 0.9):
V_{i} = V_{i} + a [r + dV_{j}  V_{i}] 


Term

Definition
 Different from Value per state V_{state}
 We keep track of a Q value for each possible stateaction pair
 This is also called the modelfree learning



Term
What is the update formula for the Qlearning? 

Definition
_{Qi,k} = Q_{i,k} + a [r + d.max(Q_{j,x}  V_{i,k}]
Where
 We're in state i, we choose action k that takes us to state j and gives us reward r
 Learning rate a = 0.1, discount factor d = 0.9
 Expected value of getting to state j is the maximum Q value we could get for any action x done at j



Term
What are the characteristics of local search? 

Definition
 Local search methods are highly general
 Means starting somewhere in a space of posibilities and iteratively trying the neighbours of our current location to see if they are better  thus myopic
 Used when we're ignorant of the global structure of our possiblity space
 Computationally inefficient
 Mirrors natural adaptation



Term

Definition
 Biological concept
 Way organisms become better suited to their environment over time
 "Survival of the fittest"



Term

Definition
 If value for one genetic value depends on the values of the other variables
 AKA epistasis (biology) interaction (Statistics), frustration of variables (engineering)
 As local correlation goes down the ruggedness of the landscape goes up and becomes more difficult to search
 Rugged landscapes are high in epistasis, whereby the fitness contribution of one parameter is modulated by others



Term
What is the randomrestart hill climber? 

Definition
 Choose a random solution
 Generate a mutated variant (e.g. flip a random symbol to some other value
 If the variant is better, it replaces it and then we repeat step 2
 If there are no improvements after N tries, go back to step 1 (but always remember best solution so far)



Term
What are the features of genetic algorithm? 

Definition
 Variation: Individuals not all the same; some random variation present
 Selection: not all individuals survive to reproduce, and some individuals reproduce more than others
 Heredity: individuals tend to be like their parents



Term
What are the components of a Genetics algorithm? 

Definition
 Population of solutions/individuals
 Mapping from genotype to phenotype
 Fitness function
 Selection procedure
 Crossover
 Mutation



Term
What are the principles of Selection in a GA? 

Definition
 Individuals with higher fitness scores are more likely to reproduce
 Can be achieved through fitnessproportionate "roulettewheel" selection
 Rankbased selection and touranment selection also used



Term
What are the principles of Crossover in a GA? 

Definition
Idea is that advantageous mutations can be shared around population
 Singlepoint
 Multipoint
 Uniform



Term
What are the principles of Mutation in a GA? 

Definition
 the source of variation
 Without mutation, crossover couls shuffle the initial set of genes around, but there would be no evolutionary novelity
 In a bitstring, mutation is implemented as a probability that any one bit will be flipped during reproduction
 Things get trickier with realvalued genotypes, and a poorly chosen mutation operator may introduce biases



Term
What are the principles of the law of uphill analysis and downhill invention? 

Definition
 Analysis  figuring out the vehicle's circuitery from outside is much tougher than
 invention  playing around with different vehicle designs to see what they doo
 A psychological consequence:
 We tend to overestimate the complexity of the mechanisms behind cognitive systems (e.g. we propose a complex modular architecture)


