We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. All current methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand. We demonstrate that such techniques tend to misestimate instances whose predictions are supposed to be highly confident. This deficiency is an artifact of the training process with SGD-like optimizers. Based on this observation, we develop an uncertainty estimation algorithm that "peels away" highly confident points sequentially and estimates their confidence using earlier snapshots of the trained model, before their uncertainty estimates are jittered. We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than the best known methods.

This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources. Specifically, we focus on the ubiquitous problem of linear regression, where strategyproof mechanisms have previously been identified in two dimensions. In our setting, agents have single-peaked preferences and can manipulate only their response variables. Our main contribution is the discovery of a family of group strategyproof linear regression mechanisms in any number of dimensions, which we call generalized resistant hyperplane mechanisms. The game-theoretic properties of these mechanisms -- and, in fact, their very existence -- are established through a connection to a discrete version of the Ham Sandwich Theorem.

In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines and Multilayer Perceptron Neural Networks. Hyperparameter optimization will be done on two datasets, a regression dataset for the prediction of cooling and heating loads of buildings, and a classification dataset regarding the classification of emails into spam and non-spam. The SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimization. By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your Machine Learning algorithms for maximal performance.

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.

As the buzz around data science grows every day, there is a slew of self-taught professionals who have kick-started the machine learning journey with Andrew Ng's online courses. Many enthusiasts are gravitating towards the computer science field. But if one wants to pursue a career in Machine Learning, they need to be familiar with statistics and linear algebra. With computer science and ML applications becoming more pervasive in everyday life, people from a non-technical background are also interested in joining the field. In this article, we have discussed in-depth roles a person from non-tech background can explore in the data science/AI field.

Face Recognition is a recognition technique used to detect faces of individuals whose images saved in the data set. Despite the point that other methods of identification can be more accurate, face recognition has always remained a significant focus of research because of its non-meddling nature and because it is people's facile method of personal identification. Face recognition algorithms classified as geometry based or template based algorithms. The template-based methods can be constructed using statistical tools like SVM [Support Vector Machines], PCA [Principal Component Analysis], LDA [Linear Discriminant Analysis], Kernel methods or Trace Transforms. The geometric feature based methods analyse local facial features and their geometric relationship.

Data Science 101 is an educational app to learn machine learning algorithms. Due to data science and artificial intelligence, new technologies are emerging and there is need for more specialization in this field. This app is a beginner guide for anyone who wants to study data science and make their own machine learning models. This app provides high quality resources for students to study data science and machine learning algorithms and also provides required code. Decision Trees 2. Includes code for developing models of various machine learning algorithms in Python.

About this course: Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data.

Apache Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. It's used to create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. If you're a data professional who is familiar with machine learning and wants to use Apache Spark for developing efficient and fast machine learning systems, then this learning path is for you. This comprehensive 2-in-1 course teaches you to build machine learning systems, perform analytics, and predictions with Apache Spark. You'll learn through practical demonstrations of use cases, clear explanations, and interesting real-world applications. Each section briefly establishes theoretical basis for the topic under discussion and then cement your understanding with practical use cases.

Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers' revenue and buyers' affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.