Conférences - Réseaux de neurones et intelligence artificielle, apprentissage automatique - États-Unis

Cognilytica

This course is valuable for anyone who wants in-depth knowledge about how to succeed with AI & ML related projects. Cognilytica’s CPMAI AI & ML Project Management Certification has no prerequisites, and is appropriate for people with different roles and levels of expertise.

Course Coordinator;     Email.: info@cognilytica.com

artificial intelligence, machine learning, course, e-learning, training course

Cognilytica

The Advanced Process Management: From Automation to Intelligence course provides fundamental understanding of automation systems and increasing levels of intelligence with step by step practical guidance. This course provides a detailed overview of Robotic Process Automation (RPA) and process automation systems, including evolution of Business Process Management (BPM) and Business Process Integration (BPI), moving to higher levels of intelligent automation including Content Intelligence-enabled Processes, Autonomous Process Management and Optimization, and Autonomous process design. This course also provides a downloadable roadmap and workbook that facilitates the automation to intelligence journey. This course is valuable for anyone who wants in-depth knowledge about how to move from automation to AI, and this course has no prerequisites, and is appropriate for people with different roles and levels of expertise.

Course Coordinator;     Email.: info@cognilytica.com

artificial intelligence, machine learning, course, e-learning, training course

Cognilytica

The Practical & Comprehensive Ethical & Responsible AI Framework Development Training & Certification provides a comprehensive deep dive into the components of ethical & responsible AI frameworks and provides a guided, step by step approach for crafting an implementable Ethical and responsible AI framework. This course dives deep into a comprehensive ethical AI framework, which standardizes ethical frameworks from over 60 organizations including governmental, corporate, and leading organizations. Using this comprehensive framework, trainees will be able to understand the range of ethical and responsible AI & data needs across societal ethics, responsible use of AI, AI systemic transparency, governance of AI systems, and algorithmic interpretability and explainability.

This course provides the "Quickstart" Cognilytica's Comprehensive Ethical AI Framework as well as additional support materials so that trainees can develop their own frameworks for AI and data projects. This course combines the best elements of ethical AI frameworks into a cohesive, well-defined set for buyers and builders of AI systems and is a great starting point for organizations looking to build their own AI framework. This course provides a certification in the Management of Ethical & Responsible AI Systems, has no prerequisites, and is appropriate for people with different roles and levels of expertise.

Course Coordinator;     Email.: info@cognilytica.com

artificial intelligence, machine learning, course, e-learning, training course, professional development

Cognilytica

Developing AI and machine learning models is just the first step to keeping AI and ML systems performing and providing their needed value to the organization. In addition, organizations are realizing the need to manage models produced by other parts of their organization of third-parties. The developing area of Machine Learning Operations (ML Ops) and additional aspects of machine learning lifecycle management has emerged to address these needs. The Managing the AI & ML Lifecycle: "ML Ops" and Beyond course provides a foundational overview of AI & ML lifecycles, concepts around MLOps, deep dive into each of the aspects of AI & ML lifecycle management, details on ML Ops needs and capabilities, and provides a guidebook for managing ML projects as they continue to iterate, as well as steps and practical guidance for how to adopt an MLOps game plan at your organization to ensure success. This course provides a certification upon successful completion, has no prerequisites, and is appropriate for people with different roles and levels of expertise.

Course Coordinator;     Email.: info@cognilytica.com

artificial intelligence, machine learning, course, e-learning, training course, professional development

Cognilytica

A well-defined and comprehensive big data strategy lays out the steps needed to become a more data-forward organization. Crafting a well-thought out and actionable strategy in the face of continual changes and demands for big data is a challenge. In the Building & Managing a Data-Forward Organization training and certification, Cognilytica provides guidelines, frameworks, and a roadmap for building a data-forward organization combined with fundamental understanding of the full Data infrastructure stack and the ecosystem of data technologies that add continual value. In this course Cognilytica provides a multistep approach to formulating a big data strategy, guidance on organizational change management, data infrastructure frameworks and a guidebook on managing a data-forward organization. This course provides a certification upon successful completion, has no prerequisites, and is appropriate for people with different roles and levels of expertise.

Course Coordinator;     Email.: info@cognilytica.com

artificial intelligence, machine learning, course, e-learning, training course, professional development

Cognilytica

The Data for AI Community is geared toward innovative companies pushing the boundaries of what’s possible with Artificial Intelligence and cognitive technologies. This community is focused on the data side of AI including: Data Engineering, Data Preparation, Data Labeling & Annotation, Sourcing and Generating Data, and All Other Topics Data-Related for AI. Join us at this monthly event for high-quality content with compelling & informative speakers and opportunities to network and connect with fellow like-minded individuals.

Gestion des connaissances, Big Data Computing,    Séminaire en ligne, webinaire

The aim of the conference CPAIOR is to bring together interested researchers from Constraint Programming (CP), Artificial Intelligence (AI), and Operations Research (OR) to present new techniques or applications, and to provide an opportunity for researchers in one area to learn about techniques in the others. A main objective of this conference series is also to give these researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems. Therefore papers that actively combine, integrate, or contrast approaches from more than one of the areas are especially solicited. High quality papers from a single area are also welcome, provided that they are of interest to other communities involved. Application papers showcasing CP/AI/OR techniques on novel and challenging applications or experience reports on such applications are strongly encouraged.

Institute for Pure and Applied Mathematics (IPAM) at UCLA

As algorithmic decisions and likelihood predictions reach ever more deeply, and with increasing consequence, into our lives, there is an increasing mandate that they be “fair”. Who counts? Machine learning algorithms learn from training data; when these are biased so are the algorithms they produce. The workshop will examine sources of sex and gender bias in data, with emphases on impoverished women; women of color; trans and non-binary persons; and older women. This program is preceded by a Graduate Summer School on Algorithmic Fairness organized by Cynthia Dwork, and Guy Rothblum (Weizmann Institute of Science).

Open Data Science Conference

The ODSC West in-person and virtual conference will feature expert-led, hands-on instruction in in-demand topics and tools, including: NLP | Python | Responsible AI | TensorFlow | PyTorch | Conversational AI | OpenAI | Cybersecurity | Data Visualization | scikit-learn | MLOps | Data Engineering | Machine Learning | and much more

You’ll also have the opportunity to dust off your social skills and reconnect with peers and colleagues during our networking and social events.

Iryna Pidkovych;     Email.: iryna@odsc.com

Data Science, Artificial Intelligence, Machine Learnng

Institute for Pure and Applied Mathematics (IPAM)

For molecular systems we know the laws of physics to extreme precision. Yet, our ability to compute properties of these systems to numerical precision is very limited. This is mainly due to two sources of computational intractability: quantum mechanics and chaos. On one side, the accurate solution of the Schrödinger equation for large molecular systems is computationally prohibited. One the other side, even if we knew the forces exactly, the dynamical equations for the atoms would exhibit chaotic behaviour, which implies that, just as the weather, we have limited capacity to predict future trajectories. Some aspects of these systems remain predictable at a macroscopic scale, but they require completely different variables, such as pressure, temperature and entropy. We call this emergent theory thermodynamics.

In the completely different discipline of machine learning, a fairly similar phenomenon takes place. The microscopic variables of an image are given by its constituent pixel values. When we analyse the image with a deep neural network (DNN), we will detect edges in the first layer, corners in the next layer, then the object parts and finally, at the top of the neural network, entire objects. We understand the world at the “emergent” level of objects and their relations, not at the level of pixels and edges. In deep learning (DL) emergence happens automatically through learning and some inductive biases such as symmetries.

A major question we want to address in this workshop is whether we can apply the same learning paradigm to the field of molecular science to learn the correct emergent variables and dynamics.

Mathématiques appliquées (en général),    Simulation

UC Santa Barbara, Kavli Institute for Theoretical Physics (KITP)

A long-standing question in the neurobiology of learning is that of timescales. The brain can associate events that are separated by seconds, minutes, hours, days, weeks, or longer. Yet, the known biological mechanisms for inducing changes in the synaptic connections between neurons are driven by neural events that are separated by typically no more than a few tens or hundreds of milliseconds. This conference will convene scientists studying the molecular, cellular, and circuit-level mechanisms used by the brain to bridge the multiple timescales across which events play out in the experience of an individual. The goal will be to assemble an up-to-date understanding of the set of biological mechanisms available in the brain to support associative learning across different timescales. The conference's emphasis on biological mechanisms will complement and support the main program’s more computational focus, and encourage an integrated analysis of statistical learning spanning molecular, cellular, circuit, and behavioral-level phenomena.

Biologie moléculaire,    Neurologie