Bayes' Theorem allows a program to infer the probabilities of likely causes from the probabilities of their effects, when what it is given are the probabilities of effects, given the causes.
This article belongs to the series "Probabilistic Deep Learning". This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e., know what they do not know. In this article, we will introduce the concept of probabilistic logistic regression, a powerful technique that allows for the inclusion of uncertainty in the prediction process. We will explore how this approach can lead to more robust and accurate predictions, especially in cases where the data is noisy, or the model is overfitting.
Machine Learning (ML) is the branch of Artificial Intelligence in which we use algorithms to learn from data provided to make predictions on unseen data. Recently, the demand for Machine Learning engineers has rapidly grown across healthcare, Finance, e-commerce, etc. According to Glassdoor, the median ML Engineer Salary is $131,290 per annum. In 2021, the global ML market was valued at $15.44 billion. It is expected to grow at a significant compound annual growth rate (CAGR) above 38% until 2029.
Abstract: We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it to measurements taken by the RAman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland. We give a detailed description of using a Bayesian method to retrieve temperature profiles including estimates of the uncertainty due to the network weights and the statistical uncertainty of the measurements. We trained our model using lidar measurements under different atmospheric conditions, and we tested our model using measurements not used for training the network. The computed temperature profiles extend over the altitude range of 0.7 km to 6 km. The mean bias estimate of our temperatures relative to the MeteoSwiss standard processing algorithm does not exceed 0.05 K at altitudes below 4.5 km, and does not exceed 0.08 K in an altitude range of 4.5 km to 6 km.
You tagged this question with the tag "Maximum Likelihood". In maximum likelihood estimation you explicitly maximize an objective function (namely the likelihood). It just so happens that for an observation that we assume to be drawn from a Gaussian random variable, the likelihood function usually takes a nice form after you take a logarithm. Then there is usually a leading negation, encouraging the entrepreneurial optimizer to switch away from maximizing the objective to minimizing the negative of objective, or roughly the "cost". For discrete maximum likelihood estimation the "cost" also has another meaningful name since it takes the same form as the euclidean distance in the observation space.
CS 221 ― Artificial Intelligence My twin brother Afshine and I created this set of illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class, which I TA-ed in Spring 2019 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Artificial Intelligence. You can help us translating them on GitHub!
Metanomic, a game economics and player analytics company, announced in September the launch of its player analytics platform Thunderstruck, using AI based on Bayesian inference and aiming to revolutionize game developers' use of behavioral data to improve retention and monetization. Metanomic is a software company founded in November 2021 by Theo Priestley, Bronwyn Williams and Evan Pappas. A comprehensive real-time economy-as-a-service platform for developers, it uses patented algorithms to easily deploy plug-and-play, interoperable and scalable game and creator economies ready for web3, metavers and play-and-earn games. The company has secured $2.9 million in pre-seed funding. On May 18, it announced the acquisition of Intoolab AI, a company specializing in Bayesian network-based artificial intelligence, to develop and improve data analysis in video games and on the Web3.
In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics.
An ML model is a collection of rules and preferences applied to a dataset, which enables computers to make predictions. Learning includes collecting data, cleaning it, and training the model using more powerful algorithms and/or new datasets. Once trained, your computer can make predictions with high accuracy over many cases. While there are techniques like gradient descent, transfer learning, batch normalisation, etc, for enhancing models, there are various algorithms that are useful for solving different types of problems and training a model. This article covers algorithms for training machine learning models, including neural networks, bayesian inference, and probabilistic inference.
My client, a world leader in the defence sector, requires an Machine Learning Algorithm Developer to join them in Bristol and work as part of a team on the development and evaluation of state-of-the-art algorithms for the guidance, control and navigation of their missile and weapon systems.
The Machine Learning Algorithm Developer will work within a team of Intelligent Systems, Autonomous Systems and Command and Control Engineers to develop and evaluate state-of-the-art algorithms across a range of domains from on-board, autonomous decision making to off-board algorithms. The work will involve the research, development, test, evaluation and implementation of algorithms that integrate into complex guided weapon systems products.
Algorithms are central to the design of sophisticated guided weapon systems products. These algorithms are developed throughout the lifecycle of the product and include research studies to investigate algorithms for future developments.
Machine Learning Algorithm Developers are involved in the lifecycle of projects, playing a pivotal role in our product developments including:
Technical development of specific algorithms or studies for key programmes.Feasibility studies, algorithm design and trade-off studies, preparing trials, trials analysis and reporting, defining architecture, validating algorithms and models.Technical assessments and investigations into a full range of issues and problems and prepare and develop solutions either solely or as a member of a project team.Engaging with the algorithm users, understanding and responding to their needs and ensure that the algorithms are fit for purpose.
You will gain exposure to a range of other related subject areas e.g. Simulation and Modelling, Software, Hardware-in-the Loop, Systems Design & Validation, Seekers & Sensors, Datalinks and Technical Quality and will be exposed to cutting-edge technological innovations, playing a meaningful role through the development of complex weapon systems.
To be considered for this role, applicants will ideally have completed (or be soon to complete) a PhD level in a related area with a good degree in a subject with strong mathematical content and programming skills e.g. Engineering, Mathematics, Physics, Computer Science, Information Engineering.
You will have previous experience in the development and practical application of algorithms, with experience in some of the following:
Robotics, data fusion, tracking/estimation, pattern discovery & recognition, statistical inference, optimisation and machine/deep learning algorithms along with real-time implementation, and/or validation & verification.
You will also have experience in some of the following: Matlab, Simulink, Stateflow, Python including PyTorch, TensorFlow, Open AI-Gym/Universe, Model Based Design.
Specific knowledge or experience in any of these areas would also be ideal:
Robotics, guidance and autonomous decision making, e.g. Routing and motion/trajectory planning, optimisation, co-ordinated guidance and control, decision theory, MDPs/POMDPs, specialist systems, game theory, decision support systems, multi-agent systemsData fusion and state estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster-Shafer theory of evidenceMachine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clusteringDeep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification, deep embeddings, recurrent Networks, natural language processingComputer Vision algorithms e.g. Structure from motion, image Based navigation, SLAM, pose estimation/recovery
Machine Learning Algorithm Developer
Salary £35-50k plus benefits DOE
Key Skills: Intelligent Systems Engineer, Intelligent Autonomous Systems Engineer, IAS Engineer, PhD, Mathematics, Algorithms, Programming, Robotics, Autonomous Decision Making, Machine Learning, Deep Learning, Data Fusion, Pattern Discovery, Pattern Recognition, Computer Vision, Machine Vision, Matlab, Simulink, Stateflow, Python, PyTorch
Due to the nature of work undertaken at our client's site, incumbents of these positions are required to meet special nationality rules and therefore these vacancies are only open to sole British Citizens. Applicants who meet these criteria will also be required to undergo security clearance vetting, if not already security cleared to a minimum SC level.
Electus Recruitment Solutions provides specialist engineering and technical recruitment solutions to a number of high technology industries. We thank you for your interest in this vacancy. If you don't hear from us within 7 working days please presume your application has been unsuccessful on this occasion. You are of course free to resubmit your CV/details in the future and we shall assess your suitability at that time.
This role is a PERMANENT position
Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess the reliability of model predictions, trigger human intervention, or decide whether a model can be safely deployed in the wild. We introduce Fortuna, an open-source library for uncertainty quantification. Fortuna provides calibration methods, such as conformal prediction, that can be applied to any trained neural network to obtain calibrated uncertainty estimates. The library further supports a number of Bayesian inference methods that can be applied to deep neural networks written in Flax.