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 resource is to support teachers and educators to run Day of AI activities in their classrooms through curriculum packages and teacher training, all of which is available at no cost to participants.
Developed by leading faculty and educators from MIT RAISE, the curriculum features up to four hours of hands-on activities that engage kids in creative discovery, discussion, and play as they learn the fundamentals of AI, investigate the societal impact of these technologies, and bring artificial intelligence to life through lessons and activities that are accessible to all, even those with no computer science or technical background.

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 study suggests that early ACL reconstruction surgery delays secondary injury to the knee meniscus compared with delayed surgery or nonsurgical treatment. The findings are published in _The_ _American Journal of Sports Medicine_. The anterior cruciate ligament, or ACL, is an important stabilizing structure in the knee. Injury to the ACL places additional stress on neighboring knee structures, such as the meniscus, increasing the risk of secondary injury. However, it’s unclear whether the type and timing of ACL reconstruction affect the risk of subsequent meniscal injury. To find out, researchers recently used machine learning to compare the risk and timing of secondary meniscal injury among patients whose ACL injuries were treated nonsurgically, with early reconstruction surgery, or with delayed reconstruction surgery. There was no significant difference in the overall occurrence of secondary meniscal injury in the patients who were treated with versus without surgery..."

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

13. Brave New World - Emerging Threats: Cybersecurity

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

This research resource suggests you some best sites of dataset collection which some of you might have known earlier. In the research field of machine learning, it is always tough to find the related dataset and if found it is always hard to filter some. Now that technologies have imporved, this article suggests some well known resources to collect your dataset related to your research. 

15. Brave New World: Ethical Considerations and Risks

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

Artificial Intelligence (AI), and the algorithmic judgment at its core, is developing at breakneck speed. This version of the popular Ethics for Engineers course focuses on the ethics issues involved in the latest developments of computer science.

This video aims to delve into the human problems brought out by issues in artificial intelligence, specifically with respect to bias. It is suitable for classroom use or as a standalone video for those who wish to understand the issue more deeply than is conventionally covered. For classroom use, we recommend watching the chapterized version of the video and working through the teaching materials provided for each chapter.

In an effort to build the capacity of the students and faculty on the topics of bias and fairness in machine learning (ML) and appropriate use of ML, the MIT CITE team developed capacity-building activities and material. This material covers content through four modules that an be integrated into existing courses over a one to two week period.

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:

"Diarrhea is common among calves and causes huge losses to the global cattle industry. Fecal microbiota transplantation (FMT) is one promising approach to prevent and treat calf diarrhea. However, achieving success with FMT is difficult because of farm management differences, a lack of good donors, and the difficulty of recipient selection. To guide more effective FMT, a recent study investigated factors related to FMT success or failure in 20 donor–recipient pairs. The overall success rate for diarrhea improvement was 70%. Selenomonas bacteria were found in both donors and recipients when FMT was successful, suggesting that Selenomonas may be a biomarker of donor–recipient compatibility, and Sporobacter was identified as a potential biomarker for good donor selection. Pairs of correlations between specific microbial taxa and metabolites were also linked to success, and low levels of pre-FMT glycerol 3-phosphate, dihydroxyacetone phosphate, and isoamylamine were predicted to facilitate good results..."

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

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:

"Environment-derived gut microbes can contribute to host health. In soil invertebrates, the gut microbiome is gradually assembled from the specific soil microecological region that the host inhabits. However, the effects of environmental stress on soil invertebrate microbiomes remain unknown. To learn more, a new study sought to characterize the gut bacterial taxa of the soil invertebrate Folsomia candida in the presence or absence of environmental concentrations of soil pollutants. Sequencing revealed that exposure to the fungicide azoxystrobin (AO), the antibiotic oxytetracycline (OTC), or their mixture (AO) increased Gammaproteobacteria abundance. The Gammaproteobacteria response was closely associated with F. candida physiological and functional indicators, such as the locomotion (HAA) index and oxidative stress..."

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

The emergence of transformer architectures in 2017 triggered a breakthrough in machine learning that today lets anyone create computer-generated essays, stories, pictures, music, videos, and programs from high-level prompts in natural language, all without the need to code. That has stimulated fervent discussion among educators about the implications of generative AI systems for curricula and teaching methods across a broad range of subjects. It has also raised questions of how to understand both these systems and the at times overstated claims made for them. This class will introduce the foundations of generative AI technology, and participants will explore new opportunities it enables for K–12 education. It will also describe and explore how an analytical frame of mind can help make clear the core issues underlying both the successes and failures of these systems. Much of the work will be project-based, involving implementing innovative teaching and learning tools and testing these with K–12 students and teachers.

This course reviews the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Throughout the semester, emphasis will return to issues surrounding the context of genomics in medicine including: what does a physician need to know? what sorts of questions will s/he likely encounter from patients? how should s/he respond? Lecturers will guide the student through real world patient-doctor interactions. Outcome considerations and socioeconomic implications of personalized medicine are also discussed. The first part of the course introduces key basic concepts of molecular biology, computational biology, and genomics. Continuing in the informatics applications portion of the course, lecturers begin each lecture block with a scenario, in order to set the stage and engage the student by showing: why is this important to know? how will the information presented be brought to bear on medical practice? The final section presents the ethical, legal, and social issues surrounding genomic medicine. A vision of how genomic medicine relates to preventative care and public health is presented in a discussion forum with the students where the following questions are explored: what is your level of preparedness now? what challenges must be met by the healthcare industry to get to where it needs to be?

Lecturers
Dr. Atul J. Butte
Dr. Steven A. Greenberg
Dr. Alvin Thong-Juak Kho
Dr. Peter Park
Dr. Marco F. Ramoni
Dr. Alberto A. Riva
Dr. Zoltan Szallasi
Dr. Jeffrey Mark Drazen
Dr. Todd Golub
Dr. Joel Hirschhorn
Dr. Greg Tucker-Kellogg
Dr. Scott Weiss

A module for the course: CS 59974: Special Topics in Artificial Intelligence - "Machine Learning in Action". Delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

A module for the course: CS 59974: Special Topics in Artificial Intelligence - "Setting up Jupyter Notebooks". Delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

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:

"Most current knowledge about gut microbiomes has been obtained from studies on mammals, while the microbiomes of fish, the most diverse group of vertebrates (~33,000 species), are less well understood. Specifically, the major influencing factors and unique features of fish gut microbiomes remain unclear. To bridge this knowledge gap, a recent study analyzed the gut contents of 227 fish representing 85 different freshwater fish (FWF) and saltwater fish (SWF) species. rRNA sequencing revealed that Proteobacteria and Firmicutes were the two most abundant phyla, indicating a different composition from the typical vertebrate microbiome, which is composed mainly of Firmicutes and Bacteroidetes. Habitat (freshwater versus saltwater) more strongly influenced the host microbiome than host taxonomy or trophic level and the microbiome taxonomic and functional profiles were better indicators of a fish’s habitat than of its taxonomy..."

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

With a focus on the creation of functional prototypes and practicing real magical crafts, this class combines theatrical illusion, game design, sleight of hand, machine learning, camouflage, and neuroscience to explore how ideas from ancient magic and modern stage illusion can inform cutting edge technology.

Module DescriptionThis educational module provides an engaging introduction to two pivotal topics within the field of information technology: Machine Learning and Data Science. Designed for students, the module emphasizes the practical applications, processes, and ethical considerations of these technologies while equipping learners with the foundational knowledge necessary to understand their roles in today’s data-driven world. By exploring both machine learning and data science, students will gain insights into how these fields work together to drive innovation across various industries.Section 1: Machine LearningMachine learning is a branch of artificial intelligence that enables systems to learn from data and make predictions without being explicitly programmed. This section will highlight its transformative impact on industries such as healthcare, finance, and entertainment, demonstrating the increasing relevance of machine learning in everyday applications.Section 2: Data ScienceData science is an interdisciplinary field that combines statistics, computer science, and domain expertise to derive insights from data. This section will explain how data science underpins machine learning by ensuring that data is collected, cleaned, and analyzed effectively.