This class was created by Brainscape user Abigail Wilkins. Visit their profile to learn more about the creator.
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Abigail Wilkins
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Decks in this class (12)
lecture 1: introduction to machine learning
Definition of machine learning,
Types of learning,
Types of supervised learning
8
cards
lecture 2: data engineering
Types of data,
What is continuous data,
What is discrete data
15
cards
lecture 3: intro to probability and statistics
What is statistical causality cau...,
What is correlation,
What is simpsons paradox
15
cards
lecture 4: systems of linear equations
Definition of matrix inverse,
The adjugate adjoint of matrix a is,
The i j entry of cofactor matrix is
10
cards
lecture 5: least squares and linear regression
What is a function,
What kinds of arguments can scala...,
What does a scalar valued functio...
16
cards
lecture 6: ridge regression and polynomial regression
Learning of a vectored function i...,
Using matrix notation what is the...,
The formulas for w are the same a...
14
cards
lecture 7: Over-fitting and bias/variance trade-off
Review what is the main aim of re...,
What is the number one rule for t...,
What is overfitting
14
cards
lecture 8: optimisation and gradient descent
In general an objective function ...,
What is gradient descent,
What are some possible convergenc...
9
cards
lecture 9: decision trees and random forest
What can decision trees be compar...,
How do we make predictions using ...,
What are the 3 types of nodes
16
cards
lecture 10: performance issues
We have learnt the building block...,
What are the common partitioning ...,
What is the purpose of splitting ...
6
cards
lecture 11: K-means clustering
Recap k means clustering is a typ...,
What is the goal of an unsupervis...,
The absence of labels on the data...
11
cards
lecture 12: Neural Networks
What is a neural network nn,
What is multilayer perception mlp,
Different activation functions ca...
14
cards
More about
EE2211
- Class purpose General learning
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