With the fast growing information available on the web, students are often greeted with countless learning materials. As such, personalization is an essential strategy for facilitating relevant learning materials to satisfy students needs. The scope of this project is to design a recommendation system by using a deep learning process for personalized learning based on a quiz module. At the end of the project, we would be able to determine how students like to learn and to evolve the learning path based on strengths to enhance the learning experiences.
The explosion of popularity of deep learning owes a lot to the success of convolutional neural networks, widely used in diverse fields including computer vision and natural language processing. Recently, the group equivariant convolutional neural network (G-CNN) was introduced, where equivariance of symmetries inherent in the data set is built in the architecture of the networks.
The aircraft flight deck has increased substantively in its complexity in recent years. The input systems are more complex, and the information feeds are much more detailed. In order for a pilot to interface effectively with the aircraft systems, the cockpit control functions must be laid out in an intuitive format. To do this, a trial and error approach is required, with meaningful input at each design phase.
The emerging techniques of machine learning and artificial intelligence are making revolutionary changes in all kinds of the industrial world. As a high-tech business solution company, Quartic.ai uses these modern techniques to help industrial manufactory companies work more efficiently. One of the challenging problems is to make the computer automatically recognize the status and behavior of the machine from the data collected by different sensors, so that people can record the history of the machine and conduct further analysis.
Lack of affordable, reliable access to the Internet in remote, rural, and Indigenous communities in Canada and internationally has led to a digital divide affecting billions. Left has developed its RightMesh technology, which lets nearby mobile devices connect without the Internet. Normally data flows from a phone, through Internet Service Providers (Telus, Shaw), to the cloud (Google, Amazon), and back to reach another person, despite being nearby. With the RightMesh network, data can flow from phone to phone to phone until it reaches the intended person.
Financial indicators of an individual firm may be in the form of time series, vectors, or even richer data, such as text or images. The purpose of this work is to explore and develop methods for dealing with such data, and in particular perform the clustering/classification of such data into similar groups. In the project the intern will develop the tools that will allow to determine whether a client should be issued a loan or not.
Nuera has been acquiring customers and data for the past 3 years and is launching an internal initiative to mine the data to find insurance claims trends. The objective of this initiative will consist of analysis and reporting on our data sets to find trends that lead to claims frequency and severity, in an effort to reduce claims costs, and consumer insurance pricing as a result. We will also be identifying customer behaviors and how those behaviors contribute to insurance purchasing and claims.
Based on the original Statistical Inventory Reconciliation(SIR) Test Method (Quantitative), K-folds cross validation is used to increase P(D) and decrease P(FA) by adjusting K, which are related to bias and standard deviation. There is a trade-off between bias and variance, with very flexible models (overfit) having low bias and high variance, and relatively rigid models(underfit) having high bias and low variance. When K is larger, we have lower bias and larger standard deviation. Also, K-folds cross validation is very useful, when data size is small.
Machine learning techniques have been applied to the financial industry for some time. They have allowed large utilities and generators to better forecast their needs, and the prices they will pay, leading to a generally more efficient grid. However, very little research has been done that could benefit power marketers, who do not have a load to serve or a generating facility to manage. The application of machine learning techniques has yielded great results in the financial industry.