AI class homework 3: Difference between revisions
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== Naive Bayes Laplacian Smoothing == | == Naive Bayes Laplacian Smoothing == | ||
<youtube>Lj9ku_w8JAE</youtube> | |||
Note: HINT: The size of the vocabulary is 11. | Note: HINT: The size of the vocabulary is 11. | ||
<youtube>evtCdmjcZ4I</youtube> | |||
== Naive Bayes 2 == | == Naive Bayes 2 == | ||
<youtube>VqJVQlsuGoA</youtube> | |||
Note: Use the probabilities from the previous part (so Laplace smoothing still applies). | Note: Use the probabilities from the previous part (so Laplace smoothing still applies). | ||
<youtube>LRQKhmXpDLI</youtube> | |||
== Maximum Likelihood == | == Maximum Likelihood == | ||
<youtube>9SDMNmgIhBE</youtube> | |||
<youtube>3lA9jrqw7_4</youtube> | |||
== Linear Regression == | == Linear Regression == | ||
<youtube>rIO9zynD__M</youtube> | |||
Note: The question is whether the line can be fit EXACTLY. | Note: The question is whether the line can be fit EXACTLY. | ||
<youtube>yTYQg1XiBEQ</youtube> | |||
== Linear Regression 2 == | == Linear Regression 2 == | ||
<youtube>5gIXtI82Olk</youtube> | |||
<youtube>ynxLGEE_Bgo</youtube> | |||
== K Nearest Neighbours == | == K Nearest Neighbours == | ||
<youtube>MhDJ47KG_Oc</youtube> | |||
<youtube>01qBi27m3Ss</youtube> | |||
== K Nearest Neighbours 2 == | == K Nearest Neighbours 2 == | ||
<youtube>SAG4-uC9BnE</youtube> | |||
<youtube>IjzpuYn7Szc</youtube> | |||
== Perceptron == | == Perceptron == | ||
<youtube>-fpVTLGoxZ4</youtube> | |||
<youtube>P88qJlIRnwI</youtube> | |||
Latest revision as of 23:56, 14 April 2018
These are my notes for homework 3 of the AI class.
Homework
Naive Bayes Laplacian Smoothing
Note: HINT: The size of the vocabulary is 11.
Naive Bayes 2
Note: Use the probabilities from the previous part (so Laplace smoothing still applies).
Maximum Likelihood
Linear Regression
Note: The question is whether the line can be fit EXACTLY.
Linear Regression 2
K Nearest Neighbours
K Nearest Neighbours 2
Perceptron