Machine Learning Mathematical Monk

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Ml 19 1 Gaussian Processes Definition And First Examples

Definition of a gaussian process. elementary examples processes.

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Ml 3 6 The Big Picture Part 2

(ml 3.6) the big picture (part 2)

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Ml 1 Machine Learning Overview And Applications

Attempt at a definition, and some applications of machine learning. playlist these learning videos is available here: http://www./my_...

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Ml 14 1 Markov Models Motivating Examples

Introduction to markov models, using intuitive examples of applications, and motivating the concept chain.

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Ml 3 1 Decision Theory Basic Framework

A simple example to motivate decision theory, along with definitions of the 0-1 loss and square loss. playlist these machine learning videos is avai...

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Ml 4 1 Maximum Likelihood Estimation Mle Part

Definition of maximum likelihood estimates (mles), and a discussion pros/cons. playlist these machine learning videos is available here: http://www.y...

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A simple example to motivate decision theory, along with definitions of the 01 loss and square loss.

How to build a decision tree for classification using the cart approach. Introduction to the kmeans algorithm for clustering. Playlist of these machine learning videos is available here.

Definition of a gaussian process. elementary examples processes.

Introduction to markov models, using intuitive examples of
applications, and motivating the concept chain.

A broad overview. playlist of these machine learning videos is
available here: http://www./my_playlists?p=d0f06aa0d2e8ffba

Ml 16 4 Why Em Makes Sense Part 1

One can arrive at the em algorithm in a natural way by trying to analytically maximize likelihood, an exponential family.

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Ml 16 2 K Means Clustering Part

Introduction to the k-means algorithm for clustering.

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Ml 2 4 Growing A Classification Tree Cart

How to build a decision tree for classification using the cart approach. playlist of these machine learning videos is available here: http://www..co...

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Ml 1 3 What Is Unsupervised Learning

A broad overview. playlist of these machine learning videos is available here: http://www./my_playlists?p=d0f06aa0d2e8ffba

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Definition of maximum likelihood estimates (mles), and a discussion pros/cons. One can arrive at the em algorithm in a natural way by trying to analytically maximize likelihood, an exponential family. (ml 36) the big picture (part 2).

Playlist these machine learning videos is avai. Introduction to markov models, using intuitive examples of applications, and motivating the concept chain. Attempt at a definition, and some applications of machine learning.

Playlist of these machine learning videos is available here. Playlist these learning videos is available here. Playlist these machine learning videos is available here.

Machine Learning Mathematical Monk

Ml 19 1 Gaussian Processes Definition And First Examples

Ml 3 6 The Big Picture Part 2

Ml 1 Machine Learning Overview And Applications

Ml 14 1 Markov Models Motivating Examples

Ml 3 1 Decision Theory Basic Framework

Ml 4 1 Maximum Likelihood Estimation Mle Part

A simple example to motivate decision theory, along with definitions of the 01 loss and square loss.

Ml 16 4 Why Em Makes Sense Part 1

Ml 16 2 K Means Clustering Part

Ml 2 4 Growing A Classification Tree Cart

Ml 1 3 What Is Unsupervised Learning

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Machine Learning Mathematical Monk