In our previous article, we saw some of the free courses in data science and AI that were launched this year. With this article, we are listing down some of the degree and diploma programmes that were launched by Indian institutes, colleges and universities along with analytics and data science training institutes. The course list includes all the graduate, postgraduate, diploma and certificate programmes that were launched in domains such as AI, data science, analytics, cybersecurity, blockchain and other new-age technologies. These courses are not free and have programme fee associated, which in most cases have been mentioned here. The list is in no particular order.

As Gen Z enters the workforce, like each generation before them, they are poised to disrupt routines, cultures, and even what it means to do business. A disruptor in its own right, COVID-19 has accelerated the transition to a remote work model, and workers across the spectrum are demanding a flexible and better-balanced future. Among all this upheaval, perhaps the most critical change coming to corporations is the opportunity to revamp learning systems to engage workers and lift the reputation of compliance and other "mandatory" training. A study conducted by Barnes and Noble College found that more than half of younger learners learn best by doing, instead of simply listening. Workers of all ages want to be involved in their own educational pathway.

Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mappings that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we propose a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of patterns present therein.

A wide range of reinforcement learning (RL) problems -- including robustness, transfer learning, unsupervised RL, and emergent complexity -- require specifying a distribution of tasks or environments in which a policy will be trained. However, creating a useful distribution of environments is error prone, and takes a significant amount of developer time and effort. We propose Unsupervised Environment Design (UED) as an alternative paradigm, where developers provide environments with unknown parameters, and these parameters are used to automatically produce a distribution over valid, solvable environments. Existing approaches to automatically generating environments suffer from common failure modes: domain randomization cannot generate structure or adapt the difficulty of the environment to the agent's learning progress, and minimax adversarial training leads to worst-case environments that are often unsolvable. To generate structured, solvable environments for our protagonist agent, we introduce a second, antagonist agent that is allied with the environment-generating adversary. The adversary is motivated to generate environments which maximize regret, defined as the difference between the protagonist and antagonist agent's return. We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED). Our experiments demonstrate that PAIRED produces a natural curriculum of increasingly complex environments, and PAIRED agents achieve higher zero-shot transfer performance when tested in highly novel environments.

We study the problem of online learning with dynamics, where a learner interacts with a stateful environment over multiple rounds. In each round of the interaction, the learner selects a policy to deploy and incurs a cost that depends on both the chosen policy and current state of the world. The state-evolution dynamics and the costs are allowed to be time-varying, in a possibly adversarial way. In this setting, we study the problem of minimizing policy regret and provide non-constructive upper bounds on the minimax rate for the problem. Our main results provide sufficient conditions for online learnability for this setup with corresponding rates. The rates are characterized by 1) a complexity term capturing the expressiveness of the underlying policy class under the dynamics of state change, and 2) a dynamics stability term measuring the deviation of the instantaneous loss from a certain counterfactual loss. Further, we provide matching lower bounds which show that both the complexity terms are indeed necessary. Our approach provides a unifying analysis that recovers regret bounds for several well studied problems including online learning with memory, online control of linear quadratic regulators, online Markov decision processes, and tracking adversarial targets. In addition, we show how our tools help obtain tight regret bounds for a new problems (with non-linear dynamics and non-convex losses) for which such bounds were not known prior to our work.

We consider evaluating the causal effects of dynamic treatments, i.e. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high dimensional covariates and is combined with data splitting to prevent overfitting. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups, e.g. among those treated in the first treatment period. We demonstrate that the estimators are asymptotically normal and $\sqrt{n}$-consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study in order to assess different sequences of training programs under a large set of covariates.

Amazon has revealed it is expanding its range of free online STEM (science, technology, engineering and mathematics) activities to keep children educate and entertained over the Christmas holidays. The tech company's selection of activities includes a new game called Cyber Robotics Challenge. This three-hour long event tasks a youngster with using maths to ensure a friend's birthday present gets delivered by an Amazon fulfilment centre robot. Amazon has also expanded its popular educational platform Maths4All to include secondary school-level activities as well as those geared towards younger pupils. Maths4All offers hundreds of worksheets on Kindle and Fire Tablets and maths challenges via Alexa.

Together, these five new machine learning services help industrial and manufacturing customers embed intelligence in their production processes in order to improve operational efficiency, quality control, security, and workplace safety. The services combine sophisticated machine learning, sensor analysis, and computer vision capabilities to address common technical challenges faced by industrial customers, and represent the most comprehensive suite of cloud-to-edge industrial machine learning services available. This is why more than a hundred thousand customers are using AWS for machine learning, and why customers of all sizes and across all industries are using AWS services to make machine learning core to their business strategy. To learn more about AWS's new industrial machine learning services, visit https://aws.amazon.com/industrial/.

NexTech AR Solutions (NexTech), a leading provider of virtual and augmented reality (AR) experience technologies and services for virtual and hybrid eCommerce, education, conferences and events announced the creation of its new Artificial Intelligence (AI) division. Through a dedicated initial team of three AI experts focused on enhancing NexTech's AI capabilities, the company aims to gain a competitive edge and create new portfolio offerings to complement its AR; streamlining operations for clients while tapping into a market that is expected to surpass $300 billion in revenues by 2024. NexTech's Mirjana Prpa, AR Product Manager, and the new head of the AI division, will drive efforts to identify, develop and deploy AI capabilities within the Company's existing AR technology and virtual experience portfolio. She will lead a growing team that will include new AI experts and a substantial number of interns. The NexTech AI program plans to create automation within the AR content creation space in order to build a self-service AR platform that easily works for everyone, turning everyone into AR creators.

Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding