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- What is the importance of probabilistic machine learning?
In terms of your specific question, Zoubin Ghahramani, another influential proponent of probabilistic ML, argues that the dominant frequentist version of ML--deep learning--suffers from six limitations that explicitly probabilistic, Bayesian methods often avoid: very data hungry; very compute-intensive to train and deploy
- Is there any difference between Random and Probabilistic?
Probabilistic means there is uncertainty in the process where the possible outcomes of some event may or may not have fair (equal) shares For example: When you throw two fair dice, the probability of getting a sum of the observed top faces greater then 4, is greater than getting a sum of less or equal to 4
- Whats the difference between probability and statistics?
One could say that statistics comprises of two parts The first part is the question of how to formulate and evaluate probabilistic models for the problem; this endeavor lies within the domain of "philosophy of science" The second part is the question of obtaining answers after a certain model has been assumed
- Probabilistic vs. other approaches to machine learning
The term "probabilistic approach" means that the inference and reasoning taught in your class will be rooted in the mature field of probability theory That term is often (but not always) synonymous with "Bayesian" approaches, so if you have had any exposure to Bayesian inference you should have no problems picking up on the probabilistic approach
- machine learning - Probabilistic programming vs traditional ML . . .
Edit 2: it seems that probabilistic is getting popular for time series modeling, where deep learning doesn't seem as effective as in other domains Edit 3 (December 2020): probabilistic programming is now becoming much more popular in a wide variety of domains, as probabilistic programming languages get better and the solvers get better and faster
- probability - What is the difference between the probabilistic and non . . .
A probabilistic approach (such as Random Forest) would yield a probability distribution over a set of classes for each input sample A deterministic approach (such as SVM) does not model the distribution of classes but rather separates the feature space and return the class associated with the space where a sample originates from
- Probability model vs statistical model vs stochastic model
The term 'Probability Model' (probabilistic model) is usually an alias for stochastic model References: 1 Using statistical methods to model the fine-tuning of molecular machines and systems Steinar Thorvaldsen, Ola Hossjer [2] Statistics (Point Estimation) - Lecture One Charlotte Wickham - Berkeley
- How is the VAE encoder and decoder probabilistic?
I think your view is correct, indeed the probabilistic nature of VAEs stems from parametrizing the latent distribution and then sampling from it I would argue that this procedure influences the whole network, making them more capable of generalization but also more prone to noisy reconstruction (often seen in GANs vs VAE comparisons)
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