AI REVISION 1

Question 1

What is the turing test? and examples

Answer 1

The turing test is a test created by Alan turing which was a test that consisted of an interrogator being connected to a person and a machine via a terminal and cannot see either of them, if the machine could trick the interrogator 30% of the time when answering questions the machine was considered intelligent.

Examples -> Siri, chatbot

Question 2

What is combinatorial explosion?

Answer 2

Occurs in numeric problems where the complexity of the problem rapidly increases caused by the increasing number of possible combinations of inputs

Question 3

What is machine learning?

Answer 3

Machine learning is a field of study that gives computers ability to learn without being explicitly programmed.

Question 4

What is supervised learning vs unsupervised learning

Answer 4

Supervised learning relies on labelled input and output training data, whereas unsupervised learning processes unlabelled or raw data.

Question 5

2 Examples of supervised and unsupervised learning

Answer 5

Supervised learning.
1. HandWriting Recognition
2. Traffic Prediction

Unsupervised Learning
1. AD Targeting (e.g. group similar costumers together)
2. Crime Hotspots (e.g. identify areas with high crime rate and report it to police management.)

Question 6

What is clustering?

Answer 6

Clustering is an unsupervised technique about grouping similar objects, the input is unlabelled data as mentioned before, and the goal is to find a natural partitioning or groups of similar data points.

Question 7

What is Association rules?

Answer 7

Association rules is an unsupervised technique that involves the input being a set of transaction records containing items and the goal is to produce dependency rules to predict occurrence of one variable based on occurrences of another variables.

Question 8

What is data mining?

Answer 8

Is the analysis of large quantities of data to discover, valid, non-obvious, useful, and ultimately humans should be able to understand pattern in data.

Question 9

How is the data split?

Answer 9

The data is usually split into two groups, training set and test set (used as estimate of how well the model works)

Question 10

What is model training?

Answer 10

Learn the model parameters

Question 11

Decision Tree nodes and goal

Answer 11

Internal nodes: Decision Rules on features
Leaf nodes: Predicted class label

Ultimate Goal -> Prediction

Question 12

Decision tree pros and cons

Answer 12

Pros - 1.Easy to interpret and explain decisions, 2. Reasonable training time, 3. Feature scaling not necessary
Cons - 1. Small variations in data can lead to very different trees, 2. over complex trees can lead to overfitting

Question 13

What is linear regression?

Answer 13

Supervised learning technique to estimate a relationship between an input variable X, and output variable X.

aim -> find a line of best fit.

Question 14

Linear regression pros and cons ->

Answer 14

Pros -> 1. easy to interpret
2. easy to train
3. works well even with limited data

Cons ->
1. only applicable if relationship is linear
2. relies on assumptions about the data

Question 15

What is model evaluation?

Answer 15

How well does the model do on the validation set.

Question 16

What is model tuning?

Answer 16

adjust model hyperparameters

Question 17

What is overfitting

Answer 17

the model fits too well to the training data which negatively impacts performance on unseen data.

Question 18

Why overfitting happens?

Answer 18

1. Training data is limited
2. Model is too complex

Question 19

How to fix overfitting?

Answer 19

1. Train with more data
2. Simplify the model.

Question 20

Explain first artificial Neurons

Answer 20

Combine binary inputs with some weights and compare it with a fixed threshold t to decide if neuron fires 1 or 0, that is done by multiplying and adding both inputs, e.g. if weight coeff was w1= 2 and w2 = 2 then x1 and x2 would be -> w1*x1 + w2+x2 = … and that results would create a logic gate.

Question 21

What is perceptron

Answer 21

Is a learning algorithm that learn the weights to help detect certain input data in business intelligence.

Question 22

Explain the learning algorithm of perceptron

Answer 22

The weights are adjusted automatically, as more instances are checked if they are misclassified then the weights are adjusted and eventually the algorithm will converge the set of weights that separates the two classes if the function is linearly separable.

Question 23

What is epoch?

Answer 23

Is the training of the neural network with all the training data for one cycle.

Question 24

What is learning rate?

Answer 24

is a hyperparameter of the model that controls how much we adjust the weights by each time an error is produced

Question 25

What are issues if learning rate is too high?

Answer 25

May jump over a good combination of weights.

Question 26

What are issues if learning rate is too low?

Answer 26

Training will take a very long time

Question 27

Pros and cons of neural networks

Answer 27

Pros -> 1. Can learn more complicated decision boundaries, 2. Can handle a large number of features.

Cons -> 1. Difficult to interpret
2. Difficult to design neural networks
3. Slow training time.

Question 28

What is the concept of search space?

Answer 28

is to find the set of all possible solutions to a problem. e.g. input a problem
return solutions to the problem.

Question 29

Construct a search tree , states, search space, operators, neighbourhood, goal test, path cost.

Answer 29

States: Possible states of the problem
Search space: all possible states reachable from the initial state
Operators - a set of actions that move one state to another.
Neighbourhood: All possible states reachable from a given state
Goal test: test if the search reached a state that solves the problem
Path cost: how much it costs to take a certain path.

Question 30

Problems with search tree

Answer 30

1. Tree size it can be combinatorial explosion
2. Computational expensive.

Question 31

What is a blind search? and their searches, e.g. BFS

Answer 31

Is a search where no information about the search space is given.

1. Breadth first search (BFS)
2. Depth First Search (DFS)
3. Uniform Cost Search (UCS)

Question 32

What are the three nodes during search in tree search?

Answer 32

1. Fringe nodes (open nodes which have not been tested but have been discovered)
2. Visited nodes (Closed nodes which have been explored and tested if they match a goal)
3. Undiscovered nodes (have not yet been discovered)

Question 33

What are the three nodes during search in tree search?

Answer 33

1. Fringe nodes (open nodes which have not been tested but have been discovered)
2. Visited nodes (Closed nodes which have been explored and tested if they match a goal)
3. Undiscovered nodes (have not yet been discovered)

Question 34

What is BFS?

Answer 34

Expand root node first and then expand all nodes in level 1 before expanding nodes on level 2
(ENQUEUE-AT-END)

completeness and optimal

Question 35

What is DFS?

Answer 35

Expand root node first and explore one branch before exploring another branch.
(ENQUEUE-AT-FRONT)
not completeness and not optimal

Question 36

What is UCS?

Answer 36

Expand root node first
always remove the smallest cost node first in the queue
(ENQUEUE-BY-COST)
completeness and optimal

Question 37

What is meant by admissible heuristic?

Answer 37

heuristic is only admissible if it never over-estimates the actual cost, e.g by always being either equal or below the actual cost

Question 38

What is A* Search?

Answer 38

A* Search is a heuristic search which the aim is to estimate the actual cost to reach the goal state in a search space

(ENQUEUE-BY-ESTIMATED-COST)

Optimal if cost is not over-estimated | Completeness

Question 39

What is the minimax search?

Answer 39

The minimax search is a game search done in a search tree which finds the most optimal move for a player.

Question 40

What is the utility function in minimax search? and when do you use it.

Answer 40

A method of measuring how good a game state is.

And you propagate utility function values from leaf node to root node.

Question 41

How to find the utility values of a move?

Answer 41

For a leaf node is just the utility value of its outcome (of the game).

And for a root node is based on the value of their child nodes.

Question 42

What is alpha-beta pruning?

Answer 42

Alpha-Beta pruning is a technique that helps the minimax search to be more efficient, by decreasing the number of nodes that are evaluated by the minimax search algorithm.

Question 43

What does Alpa and Beta mean in the Alpha-Beta Pruning

Answer 43

Alpha: the minimum score that MAX is assured of
Beta: The maximum score that MIN is assured of.