Flashcards for exam

Question 1

Linear Regression (y = mx + c)

Answer 1

It’s a basic form of regression analysis. ‘m’ represents the slope of the line, indicating how much ‘y’ changes for a unit change in ‘x’. ‘c’ is the y-intercept, showing where the line crosses the y-axis.

Question 2

Residue (ith value)

Answer 2

The difference between the observed value (yi) and the predicted value (ŷi). It’s a measure of the error in predictions.

Question 3

Unsupervised Learning Examples

Answer 3

Clustering (like K-means), Association (like Apriori algorithm), and Dimensionality Reduction (like PCA).

Question 4

Applications of Computer Vision

Answer 4

Object detection, facial recognition, medical image analysis, autonomous vehicles, and surveillance.

Question 5

Logistic Regression (Sigmoid Curve)

Answer 5

Used for binary classification problems. The sigmoid function outputs a value between 0 and 1, representing the probability of a particular class.

Question 6

Examples of Unstructured Data

Answer 6

Text files, images, videos, social media posts, and emails.

Question 7

Range for Classification

Answer 7

In binary classification, the output is typically in the range of 0 to 1, indicating the probability of belonging to a certain class.

Question 8

Image Extraction

Answer 8

Involves processing and analyzing images to derive meaningful information from them.

Question 9

Relationship Between AI and ML

Answer 9

Machine Learning is a subset of Artificial Intelligence. AI is a broader concept of machines being able to carry out tasks in a smart way, while ML is a current application of AI based on the idea that we should be able to give machines access to data and let them learn for themselves.

Question 10

Steps in Machine Learning

Answer 10

Typically include data collection, data preprocessing, model selection, training the model, model evaluation, and model tuning/deployment.

Question 11

Data Collection for Machine Learning

Answer 11

This is often referred to as ‘Data Mining’ or ‘Data Gathering’.

Question 12

Improving Facial Recognition Accuracy

Answer 12

Techniques include using more diverse datasets, applying robust algorithms, and incorporating 3D facial recognition technologies.

Question 13

Turing Test

Answer 13

A test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

Question 14

Regression Models

Answer 14

Examples include Linear Regression, Logistic Regression, and Polynomial Regression.

Question 15

Classification Model

Answer 15

A type of model that is used to separate data into different classes. This can be binary classification (like spam detection) or multi-class classification (like image categorization).

Question 16

Linear Function vs. Logistic Regression

Answer 16

Linear functions are used for predicting continuous values(relationship between height and weight), whereas logistic regression is used for binary classification (yes or no). The choice depends on the nature of the problem (regression vs classification).

Question 17

Supervised Machine Learning

Answer 17

Involves training a model on a labeled dataset. Steps include data collection, data cleaning, choosing a model, training the model, and evaluating its performance.

Question 18

Process of Classification:

Answer 18

It involves taking some kind of input, processing it, and categorizing it into a certain class or category.

Question 19

Confusion Matrix Terms:

Answer 19

True Positive: Correctly predicted positive class.
True Negative: Correctly predicted negative class.
False Positive (Type I Error): Incorrectly predicted positive class.
False Negative (Type II Error): Incorrectly predicted negative class.

Question 20

Confusion Matrix and Accuracy:

Answer 20

A tool to measure the performance of a classification model. Accuracy is calculated as (True Positives + True Negatives) / Total number of samples.

Question 21

ML for Natural Language Processing (NLP):

Answer 21

Involves applying ML techniques to understand and manipulate human language.

Question 22

Syntax and Semantics in NLP

Answer 22

Syntax refers to the arrangement of words in a sentence to make grammatical sense. Semantics refers to the meaning conveyed by a text.

Question 23

Statistical Machine Translation

Answer 23

An approach to machine translation that uses statistical models based on bilingual text corpora.

Question 24

Linear Regression Interpretation on a Graph:

Answer 24

Part 1 (Data Cleaning): It’s the process of correcting or removing inaccurate records from a dataset, improving its quality.
Part 2 (Line Interpretation): In y = mx + c, ‘m’ represents the slope (rise over run), and ‘c’ the y-intercept. The line of best fit minimizes the sum of the squares of the vertical distances of the points from the line.

Question 25

Point Residual:

Answer 25

The difference between an observed value and the value predicted by a model.

Question 26

Mean Absolute Error

Answer 26

Average of the absolute errors between predicted and actual values.

Question 27

Root Mean Square Error:

Answer 27

Square root of the average of squared differences between prediction and actual observation.

Question 28

Coefficient of Determination (R²):

Answer 28

Measures how well future outcomes are likely to be predicted by the model.

Question 29

Relative Squared Error

Answer 29

Sum of the squared differences between the actual and predicted values, normalized by the total variation in the dataset.