Learn the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning and deep learning to solve real-life problems.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Antibiotic resistance is a growing problem worldwide—and in outer space. Spaceflight can promote biofilm formation and antimicrobial resistance development, and astronauts are especially vulnerable to infection due to the unique demands of spaceflight. To support future space travel, it is critical to understand exactly how spaceflight affects microbial diversity and virulence. To learn more, researchers recently used a machine learning algorithm to analyze sequencing data from the Microbial Tracking (MT)-1 mission, which sampled microbes at eight locations on the International Space Station during three flights. The model predicted the presence of hundreds of antibiotic resistance genes (ARGs) in the 226 bacterial strains isolated from the flights, including strains of the potentially very pathogenic bacterium Enterobacter bugandensis and the food poisoning-related bacterium Bacillus cereus..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is transforming our way of life. Able to spot patterns invisible to the human eye, algorithms are learning how to make our lives easier, safer, and more fun. That power is not lost on materials researchers. During the next decade, artificial intelligence or AI-driven research could fundamentally transform how new and better materials are developed. What’s more, it might even revamp how materials research itself is carried out, enabling promising new materials and processes to be developed more quickly. Machine learning methods come in a variety of flavors, with some requiring more guidance, or “supervision,” from researchers. But, generally, a machine-learning algorithm designed to discover and understand the behavior of materials looks for patterns connecting the composition, structure, and properties of known materials..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Data Science and AI in Psychology is an interactive eTextbook that provides an introduction to data science, big data, and machine learning in psychology. It covers current trends in data science and big data in the field of psychology (Chapter 1), applications of AI in the field of psychology (Chapter 2), the psychology of data visualization (Chapter 3), data ethics (Chapter 4), an introduction to how machines learn (Chapter 5), a hands-on guide for reading and critiquing machine learning research articles that are relevant to psychological topics (Chapters 6 and 7), and an introduction to coding in Python (Chapter 8). This eTextbook also includes an introduction to ChatGPT and tips for using ChatGPT to assist with writing and coding without plagiarizing (Chapters 6 and 8). This is an interactive resource that provides students with opportunities to engage with their peers and develop critical thinking skills through problem-based, active learning.

This course about financial technology, or FinTech, is for students wishing to explore the ways in which new technologies are disrupting the financial services industry—driving material change in business models, products, applications and customer user interface. Amongst the significant technological trends affecting financial services into the 2020’s, the class will explore AI, deep learning, blockchain technology and open APIs. Students will gain an understanding of the key technologies, market structure, participants, regulation and the dynamics of change being brought about by FinTech.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"A new, fully automated approach could help spot faulty electric motors before they leave the production floor. Based on a popular machine-learning algorithm known as an autoencoder, this technique could prove invaluable to the numerous industries that produce electric motors, as well as those that rely on them. An autoencoder is an algorithm that distills, or encodes, input data down to a few key elements. It then decodes that information to reproduce the original data as closely as possible. At first glance, it might look like a simple cut-and-paste operation. But there’s more than meets the eye. The algorithm actually learns to pick out patterns that are fundamental to the structure of the original data set. For that reason, the tool is incredibly useful for cleaning up noisy data. Trained on a sufficiently large data set, an autoencoder can look at a muddled image and output a fair restoration. That ability, it turns out, is also valuable for telling a good electric motor from a bad one..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"The spread of antibiotic resistance is one of the most pressing threats facing global health. Every year, approximately 700,000 deaths worldwide can be traced to antibiotic resistance. That makes it crucial to identify antibiotic resistance genes (ARGs) and their transmission between humans and the environment. Unfortunately, because they rely on curated databases and are not sensitive to certain mutations, many methods can overlook novel ARGs. Now, a new machine learning method called HMD-ARG could provide researchers with a more powerful alternative. Taking sequence encoding as input, it determines whether an input sequence is an ARG, what antibiotic family the ARG is resistant to, its mechanism of resistance, and whether it is intrinsic or acquired, and even the sub-class of antibiotic the ARG resists, if it happens to be a beta-lactamase, all without querying against existing sequence databases. The HMD-ARG database is the largest of its kind..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This is MIT’s introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we’ll try to explain everything else along the way! Experience in Python is helpful but not necessary.

This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers have developed a new method for teaching self-driving cars how to respond to emergencies. Unlike other approaches, which teach cars to respond according to hard and fast rules, this new method trains onboard computers to react like humans do. That unique ability could make self-driving cars vastly quicker at recognizing and avoiding potential accidents. Human drivers react instinctively to road hazards—whether that’s a car that brakes suddenly or a cyclist who rushes into traffic. It’s an ability that comes from years of experience and one that’s often taken for granted. As AI experts have learned, teaching computers to do the same is notoriously difficult. Rule-based methods provide basic functionality. But they tend to be very time-consuming and can’t account for unforeseen emergencies—two tremendous liabilities for self-driving cars..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.