This lesson centers around the How AI Works: What is Machine Learning? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson students are introduced to a form of artificial intelligence called machine learning and how they can use the Problem Solving Process to help train a robot to solve problems. They participate in three machine learning activities where a robot - AI Bot - is learning how to detect patterns in fish.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

In this lab, students will train three simple neural networks using the AP Gridworld software and a perceptron neural network. The lab culminates when students have trained an autonomous car to drive around simple cars without crashing.

These are materials that may be used in a CS0 course as a light introduction to machine learning.

The materials are mostly Jupyter notebooks which contain a combination of labwork and lecture notes. There are notebooks on Classification, An Introduction to Numpy, and An Introduction to Pandas.

There are also two assessments that could be assigned to students. One is an essay assignment in which students are asked to read and respond to an article on machine bias. The other is a lab-like exercise in which students use pandas and numpy to extract useful information about subway ridership in NYC. This assignment uses public data provided by NYC concerning entrances and exits at MTA stations.

This OER material was produced as a result of the CS04ALL CUNY OER project

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:

"The human gut is home to a diverse community of microbes. Variations in the makeup of this community between individuals have been linked to diseases such as inflammatory bowel disease, diabetes, and cancer. Efforts to understand these differences have revealed three community types, or enterotypes, in humans, each representing the dominance of a single microbe. But because microbes co-mingle with many partners, studying the gut microbiome solely in terms of enterotypes misses on the highly nuanced nature of microbial interactions. Researchers recently addressed that shortcoming using a machine learning technique called latent Dirichlet allocation, or LDA. Their goal was to determine whether and how recurring microbial partnerships, or assemblages, are linked to the three enterotypes. Using gut metagenomic data gathered from 861 healthy adults across 12 countries LDA revealed three assemblages corresponding to each enterotype as well as a fourth wild-card assemblage that could be found in any gut..."

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

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

Media Literacy in the Age of Deepfakes aims to equip students with the critical skills to better understand the past and contemporary threat of misinformation. Students will learn about different ways to analyze emerging forms of misinformation such as "deepfake" videos as well as how new technologies can be used to create a more just and equitable society. This module consists of three interconnected sections. We begin by defining and contextualizing some key terms related to misinformation. We then focus on the proliferation of deepfakes within our media environment. Lastly, we explore synthetic media for the civic good, including AI-enabled projects geared towards satire, investigative documentary, and public history. In Event of Moon Disaster, an award-winning deepfake art installation about the "failed" Apollo 11 moon landing, serves as a central case study.
This learning module also includes a suite of educator resources that consists of a syllabus, bibliography, and design prompts. We encourage teachers to draw on and adapt these resources for the purposes of their own classes.
Visit Media Literacy in the Age of Deepfakes to access the learning module and educator resources. A sample of some of these materials can be found on OCW.
This course was produced by the MIT Center for Advanced Virtuality, with support from the J-WEL: Abdul Latif Jameel World Education Lab.

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:

"Increasing evidence of gut microbe-metabolite-host health interactions has prompted increasing research on the human gut microbiome and metabolome. Statistical and machine learning-based methods have been widely used to identify microbial metabolites that can be modulated to improve gut health, but whether the findings of individual studies are applicable across studies remains unclear. In a recent meta-analysis, researchers searched for metabolites whose levels in the human gut could be reliably predicted from microbiome composition, using a machine learning approach with data processed from 1733 samples in 10 independent studies. While the predictability of many metabolites varied considerably among studies, the search identified 97 robustly well-predicted metabolites that were involved in processes such as bile acid transformation and polyamine metabolism..."

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 activated sludge (AS) process is used to treat sewage or industrial wastewater. In this process, pollutants are removed by a diverse group of microorganisms. Because AS is a unique, controllable engineered ecosystem, its attributes make it attractive to ecologists studying microbial community assembly. A recent study reports a new machine learning approach that can distinguish metagenome-assembled genomes (MAGs) of AS bacteria from those of other environments. Using this method, the researchers identified some functional features that are likely viral for AS bacteria to adapt to treatment bioreactors. They found that few microorganisms are shared between different wastewater treatment plants, although some AS MAGs may have been missed due to short sequencing read length or low sequencing depth..."

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:

"People with diabetes who require basal insulin to achieve blood glucose control can be at risk of hypoglycaemia, where blood glucose levels drop too low. In randomised clinical trials (or RCTs), use of second-generation basal insulin analogues, such as insulin glargine 300 units/mL (known as glargine 300) and insulin degludec, results in similar glycated haemoglobin reductions compared with first-generation basal insulin analogues, such as glargine 100 and insulin detemir, but with less hypoglycaemia. However, it is not known whether these results translate directly to routine clinical practice, as RCTs often apply strict inclusion and exclusion criteria, meaning that they may not be generalisable to real-life situations. Electronic medical records are a source of rich real-world data, but using them to make comparisons between different treatments can be difficult because results might be biased by confounding data, something that the randomisation in RCTs is designed to minimise..."

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

This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science (https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf) as our guide for course recommendation.

It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet (linked in resource) to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.

Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

The Data Science curriculum assumes the student has taken high school math and statistics.

OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online.

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:

"Hypoglycemia, or low blood glucose, is an important risk factor for people with type 2 diabetes receiving blood glucose-lowering therapies, such as insulin. It can lead to symptoms that interfere with activities of daily living and can sometimes (though rarely) result in debilitating events, including loss of consciousness. Basal insulins are designed to help maintain stable blood glucose levels throughout the day. Data from randomized clinical trials show that newer, second-generation basal insulin analogs (such as insulin glargine 300 units per mL and insulin degludec) have lower hypoglycemia risk than first- generation basal insulin analogs (such as insulin glargine 100 units per mL and insulin detemir), while providing comparable glycemic control. However, these randomized controlled trials may not truly reflect clinical practice, as they applied strict inclusion and exclusion criteria and were conducted under strict oversight dictated by very specific protocols..."

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:

"Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the transplantation of donor derived stem cells and treats a variety of hematologic and non-hematologic disorders. Allo-HSCT patients exhibit changes in their gut microbiota and experience a range of complications post-treatment, including acute graft-versus-host disease (aGvHD). The potential roles or timing of gut microbiota reestablishment and immunological homeostasis after allo-HSCT are not known. It is also not yet known if the microbiota at other body sites plays a role. Recently, researchers ran an integrated host-microbiota analysis of the gut, oral, and nasal microbiota in children undergoing allo-HSCT. The bacterial diversity decreased in all three sites during the first month. Certain microbial taxa were already different in allo-HSCT patients before transplantation compared to healthy children. Onset of acute GvHD after treatment could be predicted from the pre-treatment microbiota composition at all three body sites..."

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 foods we eat, our biological makeup, and the microbes that dwell in our gut share a highly complex relationship. Sometimes, for example, our resident microbes can turn the beneficial nutrients and drugs we take into harmful substances. That’s the case for L-carnitine, a nutrient found in red meat and supplements, which certain bacteria metabolize into trimethylamine-N-oxide (TMAO), a compound linked to cardiovascular disease. To understand how different individuals’ microbial makeup might predispose them to harmful TMAO production researchers tested 56 individuals who received carnitine supplementation for 1 month. High-TMAO producers showed lower levels of active TMAO, likely because bacteria had already begun to break down a sizeable portion of ingested carnitine. The team also observed that TMAO productivity could be enhanced by carnitine supplementation..."

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

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.
Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

14. Brave New World: Privacy, Data Sharing and Evidence Based Policy Making

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 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.

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:

"Quantum materials are opening up a realm of possibilities in materials research. Among the best known examples are superconductivity and quantum computing. But that’s only the beginning. The same properties that make these materials unique are also enabling researchers to demystify the inner workings of the human brain. So what makes quantum materials well suited for this purpose? Unlike the free-flowing electrons in ordinary conductors or semiconductors, electrons in quantum materials show correlated behavior. That in itself has been the focus of intense physics research. But the upshot for brain research is tunable electronic behavior that can mimic the electronic signaling of neurons and the synapses between them. Most importantly, quantum materials can simulate synaptic plasticity. Plasticity is the biological ability that makes learning and memory formation possible. It’s all about timing. Connections between neurons that fire within a short, milliseconds-long time window grow stronger..."

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