6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses.
See the MIT News article Computational Thinking Class Enables Students to Engage in Covid-19 Response

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.

Textbook covering topics orienting undergraduate-level students to the major engineering disciplines (civil, computer and electronic, and mechanical) and professionalism within these disciplines.

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
This course is part of the Open Learning Library, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling.

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

Course DescriptionMachine Learning is the study of how to build computer systems that learn from experience. This course will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.

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 course covers fundamental and advanced techniques in this field at the intersection of computer vision, computer graphics, and geometric deep learning. It will lay the foundations of how cameras see the world, how we can represent 3D scenes for artificial intelligence, how we can learn to reconstruct these representations from only a single image, how we can guarantee certain kinds of generalizations, and how we can train these models in a self-supervised way.

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.

Bart Murphy (OCLC) presents 'OCLC’s Explorations in Using Machine Learning to Deduplicate and Enrich Bibliographic Data' during the AI Use Cases session at the Fantastic Futures ai4LAM 2023 annual conference. This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.

This item has files of the following types: Archive BitTorrent, Item Tile, MP3, MPEG4, Metadata, PNG, Thumbnail, h.264 720P, h.264 IA

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.