Into

Question 1

What is ML?

Answer 1

ML subset of AI techniques which learn from data

Question 2

What is Supervised Learning?

Answer 2

Algorithm that learns a function using examples of inputs and outputs.

Question 3

What is needed for supervised learning

Answer 3

labelled training data

Question 4

Examples of supervised learning

Answer 4

• classification
• regression

Question 5

What is unsupervised learning?

Answer 5

Algorithm that finds paterns in data without using labelled outputs

Question 6

What does

Example of unsupervised learning

Answer 6

Clustering

Question 7

What is classification

Answer 7

Predict discrete lavels or categories based on a group of features.
It has only two options (yes or no)

Question 8

What is regression

Answer 8

Predict a continous calue based on a group of features

Question 9

WHat are features?

Answer 9

• Measurable property or characteristic of a dataset
• they are inputs to ML models

Question 10

List 2 learning strategies

Answer 10

• Lazy learning algorithms
• Eager learning algorithms

Question 11

Lazy learning algorithms

Answer 11

• Computations are delayed until prediction.
• • New data stored during training.
• • Minimal training time, but prediction time is slow.
• • e.g. K Nearest Neighbours

Question 12

Eager learning algorithms

Answer 12

• A full model is built during the training phase.
• • More time needed to train model, but prediction is very fast.
• • e.g. Decision trees, Perceptron, SVM

Question 13

What is overfitting?

Answer 13

implies that a model is too complex for the data

Question 14

what is underfitting?

Answer 14

implies that a model is too simple for the data.

Question 15

Which algorithm should I use?

Answer 15

1. Number of input examples, N.
2. Number of features (dimensions), D, which represent each
   input example.
3. Number of target classes, M.
4. Complexity: Simple models (linear or nearest neighbours) →
   Bagging → Boosting → Neural networks
5. Predictive power
6. Interpretability
7. Stability
8. Assumptions

Question 16

Explain the difference between supervised and unsupervised learning?

Answer 16

Supervised:
* learns using examples of input or output
* required labelled data

Unsupervised:
* Finds patterns in data without using labelled data

Question 17

What is the difference between an eager learning algorithm and a lazy learning algorithm?

Answer 17

Eager:
* Full model built during training phase
* Time needed to train model
* Prediction is fast
* E.g: SVM, perceptron, decision trees

Lazy:
* Computations delayed till training phase
* new data stored during training
* minimum training time, but predition time is slow
* E.g: KNN

Question 18

Explain overfitting and underfitting.

Answer 18

Overfitting:
* Model is too complex for data

Underfitting:
* model is too simple for the data