A.I.

Question 1

which classifier is most easilt understandable to humand

Answer 1

decision tree

Question 2

You have about 1 million training examples in a 6-dimensional space.You only
expect to be asked to classify 100 test examples.

Answer 2

Nearest Neighbours is a good choice. The dimensionality is low and so
appropriate for NN. For NN, training is very slow and since there are few classications, the fact that this will be slow does not matter

Question 3

You are trying to predict the average rainfall in Bristol as a function of the
measured currents and tides in the Atlantic ocean in the previous six months.

Answer 3

This is a regression problem; least squares regression, regression trees or
locally weighted nearest neighbours are all appropriate choices

Question 4

MDP or reinforcement learning?

mental model of how the world works

Answer 4

MDP

Question 5

MDP or reinforcement learning?

Perform actions in the world to nd out and collect rewards.

Answer 5

RL

Question 6

MDP or reinforcement learning?

The goal is to find policy to collect maximum rewards.

Answer 6

MDP

Question 7

MDP or reinforcement learning?

its an offline process

Answer 7

MDP

Question 8

Value iteration vs Policy iteration

Answer 8

VALUE:

• Cheaper
• good for large state spaces and a few actions

Policy Iteration
-good for small state spaces

Question 9

When to use Policy iteration or Value iteration?

Answer 9

For small state spaces: policy evaluation using exact solution methods is often the most efficient approach, typically very fast and converges quickly.

For large state spaces, when time might be prohibitive. Value iteration is preferred.

Question 10

Reinforcement learning

Answer 10

Reinforcement learning is on-line methodology that we use when the model of world is unknown and/or rewards are delayed.

Question 11

Describe the main ideas behind the EM algorithm

Answer 11

Expectation Maximization (EM), tries to maximize the marginal likelihood

The EM algorithm works analogously

EM consists of alternating between
two steps, the E-step and the M-step.

In the E-step, we dont know what
the hidden variables are, so we compute the posterior distribution over them
given our current parameters

In the M-step, we take in our set of full
assignments with weights, and we just do maximum likelihood estimation

If we repeat the E-step and the M-step
over and over again, we are guaranteed to converge to a local optima

Question 12

Define what is meant by an admissible heuristic

Answer 12

estimate of the cost of reaching the goal state
in an informed search algorithm.

In order for a heuristic to be admissible to
the search problem, the estimated cost must always be lower than or equal
to the actual cost of reaching the goal state

Question 13

What is the main idea of Principal Component Analysis (PCA)?

Answer 13

Maximize the variance of projection along each component or minimize the
reconstruction error

Question 14

reconstruction error

Answer 14

the squared distance between the original data and

its estimate

Question 15

Why is PCA useful for visualization tasks

Answer 15

Dimensionality reduction. the first and second component
can be plotted against each other to obtain a two-dimensional representation
of the data that captures most of the variance, useful to analyze and interpret
the structure of a dataset

Question 16

Indicate how the method (K-NN) could overfit training data

Answer 16

Every point in dataset (including noise) denfies its
own decision boundary. The distance function can be chosen to do well on training set but less well on new data.
2. How can you reduce overtting? Use larger K for K-NN. Use cross-validation to choose K and the metric.

Question 17

Markov Decision process

Answer 17

Agents make actions, the successor states of those actions are
controlled by chance.

Question 18

mutual information

Answer 18

I(edibility; colour) = H(edibility) - H(edibility|colour)

Question 19

In a problem where each example has n binary attributes, to select the best
attribute for a decision tree node at depth k, where the root node is at depth
0, how many attributes are candidates for selection at this node?

Answer 19

n - k

Question 20

under what conditions is breadth first search better than depth first search

Answer 20

1. A shallow solution (path from initial state to goal state) is preferred.
2. The search tree may contain large or possibly innite branches (NOT RIGHT)
3. Very large memory space to store the search tree (or the queue) is available.

Question 21

Iterative deepening or depth first search, if:

1. A shallow solution is preferred.
2. The search tree may contain large or infinite branches.

Answer 21

ID

Question 22

Breadth-first search is a special case of uniform-cost search. (T or F?)

Answer 22

True - when the costs of all actions are equal.

Question 23

Depth-first search is a special case of best-first search.

Answer 23

True - when f(n) = - depth(n).

Question 24

Uniform-cost search is a special case of A* search

Answer 24

True - when h(n) = 0.

Question 25

MDP instances with small discount factors tend to emphasise short-term rewards (T or F?)

Answer 25

True

Question 26

If the only difference between two MDPs is the value of the discount factor then they must have the same optimal policy (T or F?)

Answer 26

False

Question 27

For an innite horizon MDP with a nite number of states and actions and with a discount factor between 0 and 1, value iteration is guaranteed to converge. (T or F?)

Answer 27

True

Question 28

When rewards are infrequent or unknown, reinforcement learning is preferred (T or F?)

Answer 28

True

Question 29

What are the main factors in order to decide whether to apply Value Iteration or Policy Iteration

Answer 29

State space size, Number of possible actions

Question 30

If a player has a dominant strategy in a simultaneous-move game, then she
is sure to get her best possible outcome in any Nash equilibrium of the game. (T or F?)

Answer 30

False - ie prisoners dilemma

Question 31

Alpha-beta pruning:

Answer 31

Alpha-beta will always find the optimal strategy for players
playing optimally. If a heuristic is available, we can expand nodes in an order that maximises pruning. Alpha-beta will require less run-time than minimax.

Question 32

Dominant strategy

Answer 32

outcome is better for the player regardless of the strategy chosen by the other player

Question 33

Nash equilibrium

Answer 33

a pair of strategies by which no player can achieve a better payoff by switching strategies, if the other player keeps the same strategy

Question 34

Zero sum game

Answer 34

the total payoff to all players is the same for every instance of the game

Question 35

As the training set size increases, what do you expect will happen with the mean training and mean testing errors?

Answer 35

The training error tends to increase. The test error tends to decrease

Question 36

Marginalisation of a leaf node yields a Bayesian network without the node.

Answer 36

True

Question 37

A Bayesian network is a directed acyclic graph

Answer 37

True

Question 38

The likelihood is the distribution over the hidden variable given the evidence

Answer 38

False

Question 39

Bayesian networks can be used to capture common reasoning patterns under uncertainty

Answer 39

True

Question 40

Do Value Iteration and Policy Iteration must always converge to the same policy?

Answer 40

In general this is true. Both algorithms are guaranteed to converge to the optimal policy. However, if there are multiple policies that are optimal (meaning they yield the same maximal values for every state), then the algorithms might diverge. Both Value Iteration and Policy Iteration will
always lead to the same optimal values.

Question 41

Propose a general, practical method for ordering

children of nodes which will tend to increase the opportunities for pruning.

Answer 41

In general we would want to use an evaluation function to estimate the values of the children and then order them so that at max nodes we order the children with larger estimated values first and at min nodes we order
the children with smaller estimated values first.