The concepts of Linear Algebra are crucial for understanding the theory behind Machine Learning, especially for Deep Learning. They give you better intuition for how algorithms really work under the hood, which enables you to make better decisions. So if you really want to be a professional in this field, you cannot escape mastering some of its concepts. This post will give you an introduction to the most important concepts of Linear Algebra that are used in Machine Learning. Linear Algebra is a continuous form of mathematics and is applied throughout science and engineering because it allows you to model natural phenomena and to compute them efficiently.

One thing I've been thinking about is how to integrate Graph Theory to my HistoryMaps project. Perhaps GT can suggest possible correlation or relationship between events.This isn't a proposal. I really would like to know what can be done using GT. Events with causal relationships are easy to spot. But, there are probably other events that are related but these signals are hidden.

For two correlated graphs which are independently sub-sampled from a common Erd\H{o}s-R\'enyi graph $\mathbf{G}(n, p)$, we wish to recover their \emph{latent} vertex matching from the observation of these two graphs \emph{without labels}. When $p = n^{-\alpha+o(1)}$ for $\alpha\in (0, 1]$, we establish a sharp information-theoretic threshold for whether it is possible to correctly match a positive fraction of vertices. Our result sharpens a constant factor in a recent work by Wu, Xu and Yu.

When you start working on a dataset that consists of pictures, you'll probably be asked such questions as: can you check if the pictures are good? A quick-and-dirty solution would be to manually look at the data one by one and try to sort them out, but that might be tedious work depending on how many pictures you get. For example, in manufacturing, you could get a sample with thousands of pictures from a production line consisting of batteries of different types and sizes. You'll have to manually go through all pictures and arrange them by type, size, or even color. The other and more efficient option, on the other hand, would be to go the computer vision route and find an algorithm that can automatically arrange and sort your images -- this is the goal of this article. But how can we automate what a person does, i.e. compare pictures two by two with one another and sort them based on similarities?

Abstract: Matrix factorization, one of the most popular methods in machine learning, has recently benefited from introducing non-linearity in prediction tasks using tropical semiring. The non-linearity enables a better fit to extreme values and distributions, thus discovering high-variance patterns that differ from those found by standard linear algebra. However, the optimization process of various tropical matrix factorization methods is slow. In our work, we propose a new method FastSTMF based on Sparse Tropical Matrix Factorization (STMF), which introduces a novel strategy for updating factor matrices that results in efficient computational performance. We evaluated the efficiency of FastSTMF on synthetic and real gene expression data from the TCGA database, and the results show that FastSTMF outperforms STMF in both accuracy and running time.

Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size. Model fitting is typically carried out via separate node-wise lasso regressions, ignoring the network-induced structure among these regressions. Consequently, the error rate is high, especially when the number of nodes is large. We propose a multi-task learning estimator for fitting Gaussian graphical regression models; we design a cross-task group sparsity penalty and a within task element-wise sparsity penalty, which govern the sparsity of active covariates and their effects on the graph, respectively. For computation, we consider an efficient augmented Lagrangian algorithm, which solves subproblems with a semi-smooth Newton method. For theory, we show that the error rate of the multi-task learning based estimates has much improvement over that of the separate node-wise lasso estimates, because the cross-task penalty borrows information across tasks. To address the main challenge that the tasks are entangled in a complicated correlation structure, we establish a new tail probability bound for correlated heavy-tailed (sub-exponential) variables with an arbitrary correlation structure, a useful theoretical result in its own right. Finally, the utility of our method is demonstrated through simulations as well as an application to a gene co-expression network study with brain cancer patients.

Probability and statistics knowledge is at the core of data science and machine learning; You'll require both statistics and probability knowledge to effectively gather, review, analyze and communicate with data. This means it's essential for you to have a good grasp of some fundamental terminologies, what they mean, and how to identify them. One such term you'll hear thrown around a lot is'distribution.' All this is in reference to is the properties of the data. There's several instances of phenomena in the real world that are considered to be statistical in nature (i.e. This means there are several instances in which we've been able to develop methodologies that help us model nature through mathematical functions that can describe the characteristics of the data.

This scratch course on stochastic processes covers significantly more material than usually found in traditional books or classes. The approach is original: I introduce a new yet intuitive type of random structure called perturbed lattice or Poisson-binomial process, as the gateway to all the stochastic processes. Such models have started to gain considerable momentum recently, especially in sensor data, cellular networks, chemistry, physics and engineering applications. I focus on the methodology and principles, providing the reader with solid foundations and numerous resources: theory, applications, illustrations, statistical inference, references, glossary, educational spreadsheet, source code, stochastic simulations, original exercises, videos and more. Below is a short selection highlighting some of the topics featured in the textbook.

Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding. The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist.

Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate graph matching with deep learning. However, there is no research to explain what role the graph matching algorithm plays in the model. Therefore, we propose an approach integrating a MILP formulation of the graph matching problem. This formulation is solved to optimal and it provides inherent baseline. Meanwhile, similar approaches are derived by releasing the optimal guarantee of the graph matching solver and by introducing a quality level. This quality level controls the quality of the solutions provided by the graph matching solver. In addition, several relaxations of the graph matching problem are put to the test. Our experimental evaluation gives several theoretical insights and guides the direction of deep graph matching methods.