Mixture of linear regressions is a popular learning theoretic model that is used widely to represent heterogeneous data. In the simplest form, this model assumes that the labels are generated from either of two different linear models and mixed together. Recent works of Yin et al. and Krishnamurthy et al., 2019, focus on an experimental design setting of model recovery for this problem. It is assumed that the features can be designed and queried with to obtain their label. When queried, an oracle randomly selects one of the two different sparse linear models and generates a label accordingly. How many such oracle queries are needed to recover both of the models simultaneously? This question can also be thought of as a generalization of the well-known compressed sensing problem (Cand\`es and Tao, 2005, Donoho, 2006). In this work, we address this query complexity problem and provide efficient algorithms that improves on the previously best known results.

Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve. Computations of likelihood ratios are analytically intractable in the majority of cases. Therefore, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed to approximate the likelihood ratios. However, the applications of MCMC methods have been limited to relatively simple object models. Supervised learning-based methods that employ convolutional neural networks have been recently developed to approximate the IO for binary signal detection tasks. In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored. Both background-known-exactly and background-known-statistically signal detection and localization tasks are considered. The considered object models include a lumpy object model and a clustered lumpy model, and the considered measurement noise models include Laplacian noise, Gaussian noise, and mixed Poisson-Gaussian noise. The LROC curves produced by the supervised learning-based method are compared to those produced by the MCMC approach or analytical computation when feasible. The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored.

With pattern recognition approach, there are many methods to implement a handwriting digits recognition task. In my previous stories, I have introduced the Linear Discriminant Analysis base on the Gaussian model maximum likelihood estimation. In this post, I apply the Logistic Regression model on the English numeral handwriting digits recognition task. In the logistic regression model, an occurrence probability of an event is represented by a logistic function. For example, in a two-class problem, the logistic sigmoid function is commonly used.

Regression is unarguably one of the most used models in data science and statistics. It is prevalent in almost every field in industry and academia. I will go through in this blog the statistical concepts involved in Simple Linear Regression i.e. regression involving only one predictor variable. The readers are assumed to have some basic knowledge of probability theory and statistics, although I have given references to the concepts. Now, we need to estimate the parameters (Beta_0, Beta_1) of model and also the value of sigma squared, which is the variance of error term.

In this article, we will explain briefly about some of the best books that can help you understand the concepts of Machine Learning, and guide you in your journey in becoming an expert in this engaging domain. Moreover, these books are a great source of inspiration, filled with ideas and innovations, granted that you are familiar with the fundamentals of programming languages. As the title explains, if you're an absolute beginner to Machine Learning, this book should be your entry point. Requiring little to no coding or mathematical background, all the concepts in the book have been explained very clearly. Examples are followed by visuals to present the topics in a friendlier manner, for understanding the vitals of ML.

You can use MATLAB with AutoML to support many workflows, such as feature extraction and selection and model selection and tuning. Feature extraction reduces the high dimensionality and variability present in the raw data and identifies variables that capture the salient and distinctive parts of the input signal. The process of feature engineering typically progresses from generating initial features from the raw data to selecting a small subset of the most suitable features. But feature engineering is an iterative process, and other methods such as feature transformation and dimensionality reduction can play a role. Feature selection identifies a subset of features that still provide predictive power, but with fewer features and a smaller model.

Dimensionality reduction is an unsupervised learning technique. Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms. There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm. In this tutorial, you will discover how to fit and evaluate top dimensionality reduction algorithms in Python.

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.

The journey of machine learning started in 1959 when Arthur Samuel introduced the term called Machine Learning. It is defined as a Field of study that gives computers the capability to learn without being explicitly programmed. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The main aim is to allow the machine to learn automatically from the examples that have been provided during learning. Now, when the term Machine Learning has become familiar to everyone and has become the most popular career and research choice as it is getting adopted by many industries, it has become important for everyone working in all industries to learn and explore Machine Learning and see what it has to offer. Machine Learning engineer is surveyed as the best job of 2019 and has shown the growth rate above 300%.

Why are there so many machine learning techniques? The thing is that different algorithms solve various problems. The results that you get directly depend on the model you choose. That is why it is so important to know how to match a machine learning algorithm to a particular problem. In this post, we are going to talk about just that. First of all, to choose an algorithm for your project, you need to know about what kinds of them exist.