PolyU Summer School_Module 1_190824

Machine learning problems can be divided into two broad categories namely,

• Supervised and unsupervised machine learning

• Linear regression and decision trees

• Artificial neural networks and convolutional neural networks

• Autonomous and semi-autonomous learning

What is machine learning?

• Computer learning algorithm

• A robotic system with autonomous capabilities

• Refers to a set of data visualization techniques

• Refers to a set of approaches for estimating f, the systematic information that X provides

  about Y

The objective of a supervised machine learning problem solution is for

• Prediction only

• Prediction and inference

• Inference only

• Prediction and not for inference

What is Python?

• A type of animal

• A machine learning algorithm

• A popular programming language

• An artificial intelligence algorithm

What is the output of

x = “awesome”

print(“Python is “ + x)

• Python is x

• Python is x = awesome

• Python is awesome

• Python is

A Python function is

• A block of code which only runs when it is called

• A block of code which runs when it is not called

• A block of code which runs all the time

• A Python code that functions well

What is linear regression?

• A machine learning algorithm based on polynomials

• A semi-supervised machine learning algorithm

• An unsupervised machine learning algorithm

• A supervised machine learning approach

Linear regression assumes that

• The dependence of Y (output) on X ₁ , X ₂ , … X_p (input) is nonlinear

• The dependence of Y (output) on X ₁ , X ₂ , … X_p (input) is semi

• The dependence of Y (output) on X ₁ , X ₂ , … X_p (input) is linear

• The dependence of Y (output) on X ₁ , X ₂ , … X_p (input) is independent

In linear regression, the intercept and slope parameters are estimated using the

• most squares approach

• least squares approach

• least cube approach

• most cube approach

Decision tress can be used to predict

• Categorical outputs only

• Categorical or numeric outputs

• Numeric outputs only

• Symbolic outputs