Early Bird Rego closes 16th February 2018. Course Overview: This course will teach you how popular machine learning algorithms work by stepping you through a series of workshops to create genetic algorithms and artificial neural networks from scratch without the need for any special plugins. Once you've learnt all the threshold concepts you'll move onto installing the Unity ML-Agents system and explore the use of Tensorflow and reinforcement learning with the Q-learning algorithm.

In the past decade, terms like machine learning, artificial intelligence, and data mining are becoming greater buzzwords as computing power, APIs, and the massively increased availability of data enable new technologies like self-driving cars. However, we've been using methodologies like machine learning in psychometrics for decades. So much of the hype is just hype. Unfortunately, there is no widely agreed-upon definition, and as Wikipedia notes, machine learning is often conflated with data mining. A broad definition from Wikipedia is that machine learning explores the study and construction of algorithms that can learn from and make predictions on data.

Welcome to the first entry in our "Innovations for Educators" series, spotlighting interesting technologies that have the potential to amplify and complement the work done by educators. Artificial intelligence (AI) is all around us. From self-driving cars to voice and facial recognition technologies to computers that can compose music, AI stands to offer unprecedented convenience in our personal lives. At the same time, AI is also transforming the world of work. From helping lawyers scan hundreds of documents and predicting which are the most useful to a case, to helping doctors analyze massive amounts of data to develop treatment plans for patients, AI can perform in seconds tasks that would normally take hours of human effort.

About this course: This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience.

WalkMe Ltd., maker of a platform for understanding and improving user experience, has added predictive analytics capabilities to its intelligent assistant technology that interprets user behavior to predict next actions and provide context-sensitive responses. The company primarily targets its technology at e-commerce scenarios in which abandonment is a common problem, as well as at internal uses such as helping employees fill out forms or complete online training courses. "We saw that most users don't ask for help, so our engagement engine understands their problems and gives guidance automatically," said Rephael Sweary, WalkMe's co-founder and president. WalkMe AI Predictive Analytics works with any enterprise software or mobile application to observe user interactions and determine the statistical likelihood that a person will abandon a process because of confusion or complexity. The software collects hundreds of data points per second in real-time, including information that isn't personally identifiable such as browser type and time of day.

Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. The first on the input sequence as-is and the second on a reversed copy of the input sequence. This can provide additional context to the network and result in faster and even fuller learning on the problem. In this tutorial, you will discover how to develop Bidirectional LSTMs for sequence classification in Python with the Keras deep learning library.

To be symmetric, the axis of symmetry is always the main diagonal of the matrix, from the top left to the bottom right. Below is an example of a 5 5 symmetric matrix. A symmetric matrix is always square and equal to its own transpose. A triangular matrix is a type of square matrix that has all values in the upper-right or lower-left of the matrix with the remaining elements filled with zero values. A triangular matrix with values only above the main diagonal is called an upper triangular matrix.

In recent times, no other technology has captured more awe than Artificial Intelligence. From handling thousands of customer inquiries on Chatbots, to running Sophie the robot, AI today plays a truly pivotal role in several industries. However, the best of AI is yet to come where enterprise applications powered by the technology will open up a wealth of opportunities for businesses to benefit from. So, what must businesses do to incorporate AI into their operations and how do they stand to benefit from the same? Before opting for AI, you should look at an adjacent industry where the technology has been used in a different capacity.

We learn driving a car -- by driving.

"ML techniques, so far, are more or less related to prediction, classification and decision-making in the networking area," says Qiang Cheng of the Artificial Intelligence Industry Association of the China Academy of ICT. Cheng was highlighting the current state of play in Machine Learning's contribution to ICT networking at an ITU workshop in Geneva, 29 January 2018. The workshop brought together experts in machine learning and ICT networking to discuss the challenges and opportunities on the agenda of the new ITU Focus Group on Machine Learning for 5G, which met for the first time from 30 January to 2 February 2018. Looking to the future, Huawei, KT, ZTE and Deutsche Telekom see great potential for Machine Learning to assist the design, operation and optimization of 5G networks. Machine Learning is expected to assist the ICT industry in meeting the challenges brought on by 5G and IoT, shifts representative of considerable increases in network complexity and the diversity of device requirements.