Week 1 Flashcards
(27 cards)
What is unsupervised learning? And what are some examples
Extracting patterns from input data with no inputted target label (ie doesn’t know
What is supervised learning? And what are some examples
Minimising the error between prediction and targets (ie training knows exactly how far from optimal and what the optimal is)
What is reinforcement learning? And what are some examples
Note: RL is neither supervised nor unsupervised
What is the difference between underfitting and overfitting?
What is a method involving the dataset to reduce overfitting
Split the dataset
What are the 3 data splits, their usages and their typical percentages
(Just train val = 80:20) Val is used to select the model (ie early stopping) test is purely for performance.
How does cross-validation work and what problem is it designed to solve
What is the curse of dimensionality
What is the no free lunch theorem
What is the difference between parametric and non-parametric models
What are the pros / cons of parametric models (and an example model)
What are the pros / cons of non-parametric models (and an example)
What is linear regression
What is the linear regression error function and what is the closed form optimisation solution
How can you use linear regression to model non-linear relationships
Functions to modify input data into features. Note: linear refers to the weights being linear
How does regression work with probabilistic models
What is maximum likelihood estimation used for
What is the math definition of MLE
What is the MLE recipe (and what is the likelihood function)
How does the least squares solution relate to the MLE solution
Why are probabilistic models often better than deterministic models
What is classification
What is a simple linear discriminant function and how does it relate to classification
Linear discriminant can be considered to be the decision boundary (ie line/hyperplane)
What is linear separability
