# Shared Flashcard Set

## Details

Comp2039 - Flashcard Set 2
Alejandro Saucedo - Flashcard Set 2
29
Computer Science
05/26/2013

Term
 What is the bayesian formula?
Definition
 [image]
Term
 What does p(A|B) mean?
Definition
 p(A|B) = p(A and B) / p(B)
Term
 What does p(A and B) mean?
Definition
 p(A and B) = p(A|B) * p(B)
Term
 What is Bayes' rule?
Definition
 p(B|A) = p(B and A) / p(A)   = p(A|B) * p(B) / p(A)
Term
 Show a joint distribution of 3 variables
Definition
 [image]
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(A|B) 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
 What is P(A or B)?
Definition
 P(A) + P(B) - P(A and B)
Term
 What are decision trees?
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
 What is entropy?
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) = Ex(I(x)) = -sum(p(x)log2(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 k-neighbour 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. K-nearest 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
 Vi = Vi + a [r + Vj - Vi] 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):   Vi = Vi + a [r + dVj - Vi]
Term
 What is Q learning?
Definition
 Different from Value per state Vstate We keep track of a Q value for each possible state-action pair This is also called the model-free learning
Term
 What is the update formula for the Q-learning?
Definition
 Qi,k = Qi,k + a [r + d.max(Qj,x - Vi,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
 What is epistasis?
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 random-restart 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 fitness-proportionate "roulette-wheel" selection Rank-based 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  Single-point Multi-point 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 bit-string, mutation is implemented as a probability that any one bit will be flipped during reproduction Things get trickier with real-valued 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)
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