From this course, you can learn Object-Oriented Programming from basics to advanced concepts. All code examples in the course are written in Java but that's doesn't mean you can't apply the knowledge from this course in other programming languages. You can easily use the knowledge from this course in any language if you want to build applications with the help of an object-oriented programming approach. There are a lot of other courses on this topic. So, why would you choose exactly this course?

Researchers have developed a new technique for performing computations straight on smart home gadgets. Their method moves the memory-intensive machine-learning model operations to a central server. The data is encoded onto light waves rather than sent hundreds of miles away from the device. Fiber optics is used to transfer the waves to a connected device, allowing massive amounts of data to be sent via a network at incredible speeds. The receiver then uses a specific optical device to quickly compute the model's components delivered by those light waves.

From this course, you can learn Testing for software engineers that includes the learning of JUnit 5, Mockito, PowerMock, and TDD approach. We hide nothing from our students! Including the source code for the home task solutions and source code of examples that were shared during the lesson. Having the source code you can just copy and paste it to run it on your local computer to understand how things work better. You are allowed to use all source code examples for learning purposes.

This book is split into 4 parts: Part I: Foundations, Part II: From Theory to Algorithms, Part III: Additional Learning Models, and Part IV: Advanced Theory. If you would like to see the content, click here. If you already have a basic understanding of Python and can apply it, you can further apply these skills and better understand the concepts of probability and statistics. It goes into depth and will take your Machine Learning journey to the next level. Statistics is a major part of Machine Learning, so the more you know the better.

Hello AI&ML Engineers, as you all know, Artificial Intelligence (AI) and Machine Learning Engineering are the fastest growing filed, and almost all industries are adopting them to enhance and expedite their business decisions and needs; for the same, they are working on various aspects and preparing the data for the AIML platform with the help of SMEs and AIML Experts to build the solutions. Things are not stopping there. To give more clarity, end users or stakeholders are looking for more clarity on solutions and justifications. This grey area is the so-called Black-Box. Now in industry, the expensive addon in this series is the so-called Explainable AI (XAI) and hope you heard about this terminology.

What did you think of the video? There are quite a few signs in the first couple of minutes of the presentation that something is not right. That is the reality of academic misconduct. There is rarely one large and obvious sign that an assessment item has been compromised. Instead, the assessment usually shows a number of strange indicators.

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research.

In this paper we discuss our experience in teaching the Robotic Applications Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and real, heterogeneous, robotic hardware. We discuss the main advantages of our solutions in terms of seamless simulation-to-real experience for students and the main shortcomings we encountered with networking and sharing GPUs to support deep learning workloads. We describe the current and foreseen alternatives to avoid these drawbacks in future course editions and propose a more cloud-native approach to deploying multiple robotics applications on a k8s cluster.

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.

Reinforcement learning (rl) is a popular paradigm for sequential decision making problems. The past decade's advances in rl have led to breakthroughs in many challenging domains such as video games, board games, robotics, and chip design. The sample inefficiency of deep reinforcement learning methods is a significant obstacle when applying rl to real-world problems. Transfer learning has been applied to reinforcement learning such that the knowledge gained in one task can be applied when training in a new task. Curriculum learning is concerned with sequencing tasks or data samples such that knowledge can be transferred between those tasks to learn a target task that would otherwise be too difficult to solve. Designing a curriculum that improves sample efficiency is a complex problem. In this thesis, we propose a teacher-student curriculum learning setting where we simultaneously train a teacher that selects tasks for the student while the student learns how to solve the selected task. Our method is independent of human domain knowledge and manual curriculum design. We evaluated our methods on two reinforcement learning benchmarks: grid world and the challenging Google Football environment. With our method, we can improve the sample efficiency and generality of the student compared to tabula-rasa reinforcement learning.