AI-LEARNING

Question 1

provides a computer with data, rather than explicit instructions. Using these data, the computer learns to recognize patterns and becomes able to execute tasks on its own.

Answer 1

Machine Learning

Question 2

given a data set of input-output pairs, learn a function to map inputs to outputs

Answer 2

Supervised Learning

Question 3

a task where the function maps an input to a discrete output.

Answer 3

Classification

Question 4

algorithm that, given an input, chooses the class of the nearest data point to that input

Answer 4

Nearest-Neighbor Classification

Question 5

algorithm that, given an input, chooses the most common class out of the k nearest data points to that input

Answer 5

K-Nearest-Neighbor Classification

Question 6

Drawback of the k-nearest-neighbors algorithm

Answer 6

Using a naive approach, the algorithm will have to measure the distance of every single point to the point in question, which is computationally expensive.

Question 7

Solution to the drawback of k-nearest-neighbors algorithm

Answer 7

Use data structures that enable finding neighbors more quickly or by pruning irrelevant observations.

Question 8

for each data point, we adjust the weights to make our function more accurate.

Answer 8

Perceptron Learning Rule

Question 9

sequences of numbers

Answer 9

Vector

Question 10

The weights and values in Perceptron Learning are represented using?

Answer 10

Vectors

Question 11

Drawback of Perceptron Learning

Answer 11

data are messy, and it is rare that one can draw a line and neatly divide the classes into two observations without any mistakes

Question 12

unable to express uncertainty, since it can only be equal to 0 or to 1.

Answer 12

Hard Threshold

Question 13

uses a logistic function which is able to yield a real number between 0 and 1, expressing confidence in the estimate

Answer 13

Soft Threshold

Question 14

they are designed to find the maximum margin separator

Answer 14

Support Vector Machines

Question 15

A boundary that maximizes the distance between any of the data points

Answer 15

Maximum Margin Separator

Question 16

Benefit of Support Vector Machines

Answer 16

they can represent decision boundaries with more than two dimensions, as well as non-linear decision boundaries

Question 17

supervised learning task of learning a function mapping an input point to a continuous value

Answer 17

Regression

Question 18

this function gains value when the prediction isn’t correct and doesn’t gain value when it is correct

Answer 18

0-1 Loss Function

Question 19

functions that can be used when predicting a continuous value

Answer 19

L1 and L2 loss functions

Question 20

L1 Loss Function Formula

Answer 20

|actual - predicted|

Question 21

L2 Loss Function Formula

Answer 21

(actual - predicted)^2

Question 22

L1 vs L2

Answer 22

L₂ penalizes outliers more harshly than L₁ because it squares the the difference

Question 23

when a model fits the training data so well that it fails to generalize to other data sets.

Answer 23

Overfitting

Question 24

process of penalizing hypotheses that are more complex to favor simpler, more general hypotheses.

Answer 24

Regularization

Question 25

This is used to avoid overfitting

Answer 25

Regularization

Question 26

a constant that we can use to modulate how strongly to penalize for complexity in our cost function

Answer 26

Lambda (λ)

Question 27

A technique for testing whether a model is overfitted by splitting data in two (testing and training set)

Answer 27

Holdout Cross-Validation

Question 28

downside of holdout cross validation

Answer 28

we don’t get to train the model on half the data, since it is used for evaluation purposes.

Question 29

we divide the data into k sets. We run the training k times, each time leaving out one dataset and using it as a test set. We end up with k different evaluations of our model, which we can average and get an estimate of how our model generalizes without losing any data.

Answer 29

k-Fold Cross Validation

Question 30

given a set of rewards or punishments, learn what actions to take in the future

Answer 30

Reinforcement Learning

Question 31

model for decision-making, representing states, actions, and their rewards

Answer 31

Markov Decision Processes

Question 32

Reinforcement Learning as a Markov Decision Process

Answer 32

- Set of states ***S*** - Set of actions ***Actions(S)*** - Transition model ***P(s’ | s, a)*** - Reward function ***R(s, a, s’)***

Question 33

method for learning a function Q(s,a), outputs an estimate of the value of performing action a in state s

Answer 33

Q-Learning

Question 34

Q-Learning Process

Answer 34

- The model starts with all estimated values equal to 0 ***(Q(s,a) = 0 for all s, a)***. - When an action is taken and a reward is received, the function does two things: 1. It estimates the value of ***Q(s, a)*** based on current reward and expected future rewards, and 2. Updates ***Q(s, a)*** to take into account both the old estimate and the new estimate. This gives us an algorithm that is capable of improving upon its past knowledge without starting from scratch.

Question 35

an algorithm that completely discounts the future estimated rewards, instead always choosing the action a in current state s that has the highest Q(s, a).

Answer 35

Greedy Decision-Making

Question 36

Explore vs. Exploit tradeoff

Answer 36

A greedy algorithm always exploits, taking the actions that are already established to bring to good outcomes. However, it will always follow the same path to the solution, never finding a better path. Exploration, on the other hand, means that the algorithm may use a previously unexplored route on its way to the target, allowing it to discover more efficient solutions along the way.

Question 37

In this type of algorithm, we set ε equal to how often we want to move randomly. With probability 1-ε, the algorithm chooses the best move (exploitation). With probability ε, the algorithm chooses a random move (exploration).

Answer 37

ε (epsilon) greedy algorithm

Question 38

Technique used in q-learning when game has multiple states and actions, making it computationally demanding.

Answer 38

function approximation

Question 39

given input data without any additional feedback/information, learn patterns.

Answer 39

Unsupervised Learning

Question 40

organizing a set of objects into groups in such a way that similar objects tend to be in the same group

Answer 40

clustering

Question 41

Applications of Clustering

Answer 41

- Genetic Research - Image Segmentation - Market Research - Medical Imaging - Social Network Analysis

Question 42

algorithm for clustering data based on repeatedly assigning points to clusters and updating those clusters’ center

Answer 42

k-means Clustering