Workshop 08/25/2021

In this third workshop in linear algebra, we will investigate the link between Principal Component Analysis and the Singular Value Decomposition.

Along the way, we are introduced to several linear algebra concepts including linear regression, eigenvalues and eigenvectors and conditioning of a system. We will use shared python scripts and several examples to demonstrate
the ideas discussed.

This workshop builds on the previous 2 workshops in linear algebra (PLEASE INCLUDE THE LINKS), and we will assume that the linear algebra concepts introduced in those workshops are familiar to the audience. They include:
vector algebra (including inner products, angle between vectors), matrix-vector multiplications, matrix-matrix multiplications, matrix-vectors solves, singularity,
and singular values.

Welcome to the world of artificial intelligence (AI) and augmented reality (AR)! This workshop explains AI and AR via hands on exercises where you will interact with your augmented world. Artificial intelligence (AI) is used for a variety of applications in various industries. AI can be combined with other technologies to assist with understanding implications of certain aspects of applications. In this hands-on workshop, you’ll explore how pose estimation results implemented using deep learning are impacted based on a location provided with augmented reality. You’ll see how these combined technologies provide insight into how poses could be interpreted differently based on a scene. You’ll also consider the consequences of using AI for applications that are different from its originally intended use, which could lead to both technical and ethical challenges.
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Many of the systems we study today can be represented as graphs, from social media networks to phylogenetic trees to airplane flight paths. In this workshop we will explore real-world examples of graphs, discussing how to extract graphs from real data, data structures for storing graphs, and measures to characterize graphs. We will work with real examples of graph data to create a table of values that summarize different example graphs, exploring values such as the centrality, assortativity, and diameter of each graph. Python code will be provided so that attendees can get hands-on experience analyzing graph data.

Recommender systems are playing a major role in e-commerce industry. They are keeping users engaged by recommending relevant content and have a significant role in driving digital revenue.
Following tremendous gains in computer vision and natural language processing with deep neural networks in the past decade, the recent years have seen a shift from traditional recommender systems to deep neural network architectures in research and industry.
In this workshop, we focus on temporal domain from perspective of both traditional recommender systems and deep neural networks. We first start with the classic latent factor model. We introduce temporal dynamics in the latent factor model and show how this improves performance. We then move into sequential modelling using deep neural networks by presenting state-of-the-art in the field and discuss the advantages and disadvantages.