This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main. Markowitz-model is the first step. One of the most elegant scientific discoveries in the 20th century is the Black-Scholes model: how to eliminate risk with hedging.

Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Django is a fantastic web framework built with the Python programming language. With Django you can create websites and web apps very quickly and efficiently. Django is very fast,secure and scalable making it an attractive framework choice for thousands of developers. Python did not get it's name from the snake but rather from the British comedy group known as Monty Python.

Scala is a powerful multi-purpose programming language that is much appreciated for its simple object-oriented, functional style. This video starts with recipes that explore core language principles--covering topics such as strings, classes, types, methods, and arrays--before getting into concepts such as Object Oriented Programming. From there, you'll learn about functional programming techniques and how to handle files and processes. You'll go on to master concurrency in Scala, making use of the Akka framework. You'll learn about working with databases, and then about Reactive programming in Scala and how to use it to build robust microservices and distributed systems.

Tech giant Google has partnered with NITI Aayog to provide an artificial intelligence (AI)-based skill training opportunity to Indian startups as well as to budding entrepreneurs. The two parties have signed a statement of intent (SoI) to train and incubate Indian startups specialising in AI-based technology. The beneficiaries of the programme will include startups and students (graduates and engineers) across universities and colleges. The training will be imparted through online and developer-run courses in the form of study groups, NITI Aayog said in a media statement. NITI Aayog CEO Amitabh Kant said, "NITI's partnership with Google will unlock massive training initiatives, support startups, and encourage AI research through PhD scholarships, all of which contributes to the larger idea of a technologically empowered New India."

JAXenter: The term'intelligence' is not easy to understand. What's the best way to explain it and how can we apply it to machines? Marisa Tschopp: Human intelligence has been a very controversial topic and has undergone dramatic changes in history since the beginnings in the early 19th century. Intelligence gained importance especially in the educational context as these "mental abilities" were the best predictors for success in school and aimed to place students into the right classes. There are various, very elaborated theories, that define human intelligence.

D-Wave announced "Quadrant," a new business unit that will provide machine learning services powered by both CPU/GPUs and its quantum annealing computer. D-Wave's Quadrant algorithms will be able to more efficiently provide accurate results with less training data compared to classical deep learning solutions that require significant amounts of labeled data. One of the promises of quantum computers has been that they are so much better at calculating multiple possibilities at the same time and finding the "optimum" result for a variety of problems. D-Wave's quantum annealing computer (a more specialized kind of quantum computer) has already been used in real-world applications such as optimizing traffic flow. Most of the machine learning (ML) or artificial intelligence (AI) solutions out there currently need millions and millions of data points in order to come up with an accurate model that can then be used in the learn world effectively.

Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificial intelligence, however, one has not fully explored the role of teaching, and pays most attention to machine \emph{learning}. In this paper, we argue that equal attention, if not more, should be paid to teaching, and furthermore, an optimization framework (instead of heuristics) should be used to obtain good teaching strategies. We call this approach `learning to teach'. In the approach, two intelligent agents interact with each other: a student model (which corresponds to the learner in traditional machine learning algorithms), and a teacher model (which determines the appropriate data, loss function, and hypothesis space to facilitate the training of the student model). The teacher model leverages the feedback from the student model to optimize its own teaching strategies by means of reinforcement learning, so as to achieve teacher-student co-evolution. To demonstrate the practical value of our proposed approach, we take the training of deep neural networks (DNN) as an example, and show that by using the learning to teach techniques, we are able to use much less training data and fewer iterations to achieve almost the same accuracy for different kinds of DNN models (e.g., multi-layer perceptron, convolutional neural networks and recurrent neural networks) under various machine learning tasks (e.g., image classification and text understanding).

To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, novel algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the stochastic and adversarial forms of jamming, respectively. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S and SAVE-A can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior information on future jamming and server security risks, the proposed schemes can achieve ${\cal O}\big(\sqrt{T}\big)$ regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S and SAVE-A offer impressive improvements on the sublinear regret, which is guaranteed by what is termed "value of cooperation." Effectiveness of the proposed schemes is tested on both synthetic and real datasets.

AI, Machine Learning, and Deep Learning are rapidly evolving and transforming many industries. Andrew Y. Ng is one of the leading minds in the field - he is a co-Founder of Coursera, former head of Baidu AI Group, and a former head of Google Brain. He is writing a book, "Machine Learning Yearning" (you can get a free draft copy), to teach you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer.

OpenCV is a native cross-platform C library for Computer Vision, Machine Learning, and image processing. It is increasingly being adopted for development in Python. This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset.