A UTMIST Project by Hanni Ahmed, Maanas Arora, Nabil Mohamed, Thomas Zeger, (Timothy Kwong), and Yan Zhu. Introduction Cardiovascular disease (CVD) is the leading cause of death in the world. According to the World Health Organization, about 17.9 million people died from CVDs in 2019, representing 32% of all global deaths…

A UTMIST Project by Justin Tran, Fernando Assad, Kiara Chong, Armaan Lalani, and Eeshan Narula. Introduction Recognizing handwritten text is a long-studied problem in machine learning, with one of the most well-known datasets being the MNIST [1] for handwritten digits. While recognizing individual digits is a solved problem, scientists have been…

A UTMIST Project by Maliha Lodi, Afnan Rahman, Kevin Qu, Omer Raza Khan, Yinan Zhao, and Matthew Zhu. Project Overview An organization is only as good as its employees, as they provide the company with its competitive advantage. As a result, it is imperative that companies understand how to retain their top…

A UTMIST Project by Arsh Kadakia, Sepehr Hosseini, Matthew Leung, Arahan Kadakia, and Leo Li RealTime is a machine learning project for predicting home prices in the GTA. It is a significant improvement on last year’s project, RealValue, by focusing on more data than ever before. Thanks to the effective…

A UTMIST Project by Noah Cristino, Bradley Mathi, Daniel Zhu, and Mishaal Kandapath. Smile Detector detects smiles in images using a new multi-step approach, which consists of performing facial recognition and then running the detected facial vectors through our custom C-Support Vector Classification model. By reducing the model input size…

Written by Charles Yuan. A discussion on the paper title “Adaptive Aggregation Networks for Class-Incremental Learning”. Introduction When you really think about it, deep learning algorithms are fundamentally restricted from achieving true artificial general intelligence. For those who have ever constructed and trained a neural network, you would likely be familiar…

Written by Natalie Volk. A discussion on the paper title “Causal Inference in medicine and in health policy”. Causal inference is a major area of research in machine learning, aiming to incorporate an understanding of cause-and-effect into AI models. By doing so, researchers believe that machine learning can help improve…

Written by Charles Yuan. A discussion on the paper title “Quantum convolutional neural network for classical data classification”. Introduction “First I will take the skies from the human race! Military aircraft, civilian planes, space capsules, rockets, ICBMs. On my order, every country in the world will become nailed to the ground…

Written by Smaran Iyer. An overview of one of the most important data analysis tools. Introduction Machine learning deals with lots of data. Oftentimes, we work with many variables, which can lead to issues regarding the efficiency of the program. One helpful technique to mitigate this problem is dimensionality reduction. Somehow…