Machine vision. Data wrangling. Reinforcement learning. What do these terms even mean? In AI 101, MIT researcher Brandon Leshchinskiy offers an introduction to artificial intelligence that’s designed specifically for those with little to no background in the subject. The workshop starts with a summary of key concepts in AI, followed by an interactive exercise where participants train their own algorithm. Finally, it closes with a summary of key takeaways and Q/A. All are welcome!

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

This course presents real-world examples in which quantitative methods provide a significant competitive edge that has led to a first order impact on some of today’s most important companies. We outline the competitive landscape and present the key quantitative methods that created the edge (data-mining, dynamic optimization, simulation), and discuss their impact.

Students learn about various crystals, such as kidney stones, within the human body. They also learn about how crystals grow and ways to inhibit their growth. They also learn how researchers such as chemical engineers design drugs with the intent to inhibit crystal growth for medical treatment purposes and the factors they face when attempting to implement their designs. A day before presenting this lesson to students, conduct the associated activity, Rock Candy Your Body.

The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches.

This is a textbook for teaching data science using the Python programming language at a general first year undergraduate level.

This is a textbook for teaching data science using the R programming language at a general first year undergraduate level.

This covers the implementation of database clustering through Open Source technologies. It is designed to teach the students on how to get, install and configure the required software and eventually set-up the cluster. It also provides an example on how a web application connects randomly to any database server in the cluster and still gets the same data. Through this example, high data availability solution is clearly demonstrated in the sense that if and when one database server in the cluster is down, the other database server can continue providing the needed data.

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

This project presents the method for the segmentation and detection of tumor of Magnetic Resonances brain images using intuitionistic fuzzy representation and intuitionistic fuzzy divergence method. In this proposed method, skull stripping is carried out for the removal of unwanted portion from the brain image using morphology.  A Restricted equivalence function from automorphisms is used for intuitionistic fuzzy representation of image. Sugeno type intuitionistic fuzzy generator is used to calculate non-membership and hesitation degree. A new distance measure, Intuitionistic Fuzzy Divergence is used to find the optimum threshold to detect the brain tumour from MR images. The results showed a much better performance on poor illuminated brain MR images, where the brain tumor is detected properly.

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.

El OA presenta el método de agrupamiento K-medias mostrando sus características, estrategia y procedimiento. El estudiante debe poseer conceptos básicos sobre métodos de agrupamiento aglomerativos y partitivos.

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

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:

"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."

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

Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others.
For each concept – a new representation or an estimation algorithm – a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.