Questions tagged [machine-learning]
How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?
3,385 questions
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Optimal lags in neuralprophet model [closed]
I am using neuralprophet model in time series data having 2 columns( ds and y). ds is timestamp and y is numerical column. As I am using hyperparameter tuning , so how to select optimal n_lags value ...
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Verifying One-vs-All Precision and Recall calculations from a multi-class confusion matrix
I am studying multi-class classification metrics and want to confirm the correct way to compute them from a confusion matrix.
A weather classifier labels days as Sunny, Rainy, Cloudy. The test results ...
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Notation Convention in Linear Models
Notation Convention in Linear Models: Why $\theta^\top x$ instead of $\theta x$?
Question:
I'm working with CMU 10-414 Lecture 2 and I'm curious about the notation convention used to represent the ...
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How to quickly come back up to speed on math I used 50+ years ago? [closed]
I'm 72, just got laid off, and am going back to school to learn machine learning. This is going to require higher maths than I took in 1971, which was Calculus I. I'm finding that mental reflexes ...
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Integration by parts from perceptron capacity calculation
I am working through Chapter 6 of the book Statistical Mechanics of Machine Learning by Engel and Van den Broeck. I am stuck on the following integral, going from line 6.13 to line 6.14 of the book.
I ...
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4th Order Tensor multiplication Rules for Sparse Regression analysis
I am working on a problem which involves working with stress and deformation tensors of the order 4. I have a set of data at different time steps for 20 cases and each element stress is 3x3 matrix, so ...
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Does the union of two datasets form a mixture distribution? [closed]
I have two datasets: $A := \{X_i\}_{i=1}^{n_a}$ sampled from distribution $P_A$, and $B := \{X_j\}_{j=1}^{n_b}$ sampled from distribution $P_B$.
Let $n = n_a + n_b$ be the total sample size, and ...
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Understanding BottleNeck Block in ResNet [closed]
I’m new to machine learning and trying to strengthen my understanding and coding skills for neural networks. Recently, I was exploring the ResNet architecture and found this article really helpful: ...
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Why is X following $\mathcal{N}(\mu + \Lambda z, \Phi)$ in the Factor Analysis model?
I’m working through some notes on Factor Analysis and I noticed something that confused me.
We have
$ X = \mu + \Lambda z + \epsilon $
with
$z \sim \mathcal{N}(0,I_s)$,
$\epsilon \sim \mathcal{N}(0,\...
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Eigenvalues and eigenfunctions of periodic kernel
The periodic kernel which is sometimes used in Gaussian process regression models looks like
$$
k(x,x') = \sigma^2 \exp \left(-\frac{2 \sin^2(\pi|x-x'|/p)}{\ell^2}\right)
$$
for parameters $\sigma^2$, ...
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Machine learning: what is the proper name for derivative of a function against a matrix?
In machine learning, it is typical to see a so-called weight matrix. As a low-dimensional example, let this matrix be defined as,
$$W = \begin{bmatrix} w_{11} & w_{12} \\\ w_{21} & w_{22} \end{...
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Jensen's inequality vs concavity of $\log(f(x))$: why EM lower bound works for GMM?
I have a doubt about the relationship between Jensen's inequality and the concavity of a composite function, specifically $\log(f(x))$.
Let $f: \mathbb{R}^n \to \mathbb{R}_{>0}$ be a positive ...
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Decision boundary in linear classification models
The decision boundary, $y = \mathbf{w}^T\mathbf{x} + b = 0$, is the decision boundary in linear classification models. When $\mathbf{x} \in \mathbb{R}^2$ and $\mathbf{w} \in \mathbb{R}^2$, then $y \in ...
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Linear model classification
I am trying to understand the geometry for linear classification models. A linear model, according to Bishop's books, is defined as: $\mathbf{y} = \mathbf{w}^T \mathbf{x} + w_0$.
For instace we have ...
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Density function of reverse diffusion process
All the prcoesses involved are continuous Markov process. The reverse diffusion and forward diffusion traverse identical trajectories in reverse temporal order.
In the Machine Learning paper Deep ...
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