The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills. We learn the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. Moreover, our framework permits skill discovery even from offline data, thereby reducing the need for excessive real-world interactions. We demonstrate empirically that LiSP successfully enables long-horizon planning and learns agents that can avoid catastrophic failures even in challenging non-stationary and non-episodic environments derived from gridworld and MuJoCo benchmarks.

Thompson sampling (TS) has emerged as a robust technique for contextual bandit problems. However, TS requires posterior inference and optimization for action generation, prohibiting its use in many internet applications where latency and ease of deployment are of concern. We propose a novel imitation-learning-based algorithm that distills a TS policy into an explicit policy representation by performing posterior inference and optimization offline. The explicit policy representation enables fast online decision-making and easy deployment in mobile and server-based environments. Our algorithm iteratively performs offline batch updates to the TS policy and learns a new imitation policy. Since we update the TS policy with observations collected under the imitation policy, our algorithm emulates an off-policy version of TS. Our imitation algorithm guarantees Bayes regret comparable to TS, up to the sum of single-step imitation errors. We show these imitation errors can be made arbitrarily small when unlabeled contexts are cheaply available, which is the case for most large-scale internet applications. Empirically, we show that our imitation policy achieves comparable regret to TS, while reducing decision-time latency by over an order of magnitude.

In many academic settings, medical students start their scientific work already during their studies. Like at our institution, they often work in interdisciplinary teams with more or less experienced (postgraduate) researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. All of them should be taught good research practices as an integral part of their education, especially in terms of statistical analysis. This includes reproducibility as a central aspect of modern research. Acknowledging that even educators might be unfamiliar with necessary aspects of a perfectly reproducible workflow, I agreed to give a lecture series on reproducible research (RR) for medical students and postgraduate pharmacists involved in several areas of clinical research. Thus, I designed a piloting lecture series to highlight definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, I encountered several practical obstacles. In this article, I focus on this working example to emphasize the manifold facets of RR, to provide possible explanations and solutions, and argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. I therefore hope these experiences are helpful to raise awareness among educators and students. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.

This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.

Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games. The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction.

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from

I recently started to follow an exciting and mind-bending philosophy online course at MIT called Minds and Machines. The course is a thorough, rigorous 12 Weeks Learning Path introduction to contemporary philosophy of mind, exploring consciousness, reality, artificial intelligence (AI), and more. It is definitively one of the most in-depth philosophy courses available online that I ever frequented. The first effect of starting study philosophy at Massachusetts Institute of Technology is that I'm asking more challenging questions… the second effect is that I'm writing more about those questions. I'm in this moment, exploring the relationship between the mind and the body, the capacity of computers to think, the way we perceive reality, and the perspective of the existence of a science of consciousness. As a first result, I've started to pay particular attention to one specific question that definitively has a lot to relate to my daily work as an AI expert: what is intelligence?

This course consists of lectures, exercises involving multiple-choice tests (MyCourse Quizzes), and a student project which will be peer-graded. You can freely collect points via the various course activities (quizzes and/or projects). In particular, you can reach the top grade solely by achieving full points in the multiple-choice tests or via the student project. There is no minimum requirement for any of the course activities. Thus you can freely decide where (quizzes and/or student project) to collect points.

Free Coupon Discount - Knowledge Graph solution development using TigerGraph, Knowledge Graph Solutions Created by Neena Sathi Preview this Course GET COUPON CODE You will be able to understand and document the use case for knowledge graph solution You will be able to Design a Knowledge Graph solution You will be able to Design / extract data from Knowledge Graph data sources. You will be able to Design / Build key knowledge graph solution components and analytics Finally, You will be able to Prototype a graph analytics experience and document your understanding on Knowledge Graph Insights using a "Rapid Prototyping of Knowledge Graph Solutions using TigerGraph" course will help you strategize knowledge graph use cases and help you build or prototype a use case for your knowledge graph engagement. This course includes - How to define Graph Use Case - How to set up Sandbox using TigerGraph for your Graph use case - How to develop and execute structured graph queries - How to define elastic or higher level graph representation - Finally how to connect your graph solution with other solution components using Python. Who this course is for: Management, strategy and business analyst professionals Architects, technical leads and system analysts from IT organization Senior year undergraduate and graduate students in Business, Analytics, and IT Vendors, consultants and service providers for Graph Analytics 100% Off Udemy Coupon . You will be able to understand and document the use case for knowledge graph solution You will be able to Design a Knowledge Graph solution You will be able to Design / extract data from Knowledge Graph data sources.

This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions, decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.