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Decks in this class (14)
Course Presentation - 00
Define machine learning 1,
Give some examples of application...
2
cards
Overview ML - 01
What is machine learning 1,
A spam filter based on machine le...,
What are the different types of m...
74
cards
Probabilities and Statistics - 02
Probability and statistics are fu...,
Probability helps us quantify the...,
Statistics helps us summarize and...
14
cards
Neural Networks - 03
Hand engineered features are time...,
Neural networks date back decades...,
The purpose of activation functio...
35
cards
Mixture Models - 04
In a latent variable model we ass...,
What are the advantages of the la...,
A lvm is any probabilistic model ...
49
cards
Bayesian Networks - 05
What is probabilistic modeling 1,
The only way we can manipulate di...,
The explicit representation of th...
40
cards
Markov Networks - 06
Give an example of a situation wh...,
What is the main disadvantage of ...,
Z is a normalizing constant that ...
39
cards
Exact Inference - 07
What is marginal inference 1,
What is maximum a posteriori map ...,
Inference is not challenging true...
33
cards
Approximate Inference - 08
In practice the probabilistic mod...,
What are the 2 main families of a...,
____________ methods have histori...
27
cards
Variational Inference - 09
Most sampling based inference alg...,
What are the shortcomings of samp...,
Sampling based inference approxim...
28
cards
Learning PGM - 10
What are the 2 approaches to the ...,
The learning task should be viewe...,
Define parameter learning 3
43
cards
Hidden Markov Models - 11
Sequential data often arise throu...,
Sequential data can also arise in...,
What does the independent and ide...
29
cards
Reinforcement Learning - 12
Define reinforcement learning 1,
In the real world the determinist...,
If we increase the probability of...
55
cards
Deep Reinforcement Learning - 13
In dl algorithms how do we obtain...,
In dl algorithms how can directly...,
What s a possible but very ineffi...
10
cards
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Advanced Machine Learning
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