This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

This resource is a Hands-On course to teach Apps Development to students who may not have any programming knowledge. This course has no pre-requisites. It’s time to add the 4th R – Reading, wRiting, aRithmetic and algoRithmic thinking. In a world where the majority of new jobs require science, technology and math skills, it is time our Liberal Arts majors get IT (Information Technology)! While employers recognize and value the importance of liberal education and the liberal arts, they also want liberal arts graduates who are not digitally challenged. Many employers report a “skills gap” as they have trouble finding recent graduates qualified with ample digital skills to fill various positions. Meanwhile, a national educational movement in computer coding instruction is growing at lightning speeds in schools across the US and many consider coding more like a basic life skill (which might someday lead to a great job) rather than an extracurricular activity. App Inventor (AI) serves to narrow this skills gap and increase the versatility of students to become active creators of technology and “digitally” ready for the workplace rather than just being passive consumers of technology. Sales of hand-held devices (smartphones, tablets and phablets) are exploding. These on-line, social, and increasingly mobile computing devices are ubiquitous and offer visual, tactile and personal experiences as never before. Mobile devices in our education landscape are digital and portable - with multimedia capabilities to access the Internet, and are drastically changing the ways we teach and learn. Developing applications for such devices enables digital natives to experience mobile technology as active creators rather than just passive consumers of technology.
Learning Goals
Learn Apps Development
Learn Digital Skills (essential for a Liberal Arts major)

Welcome to the Android developer guides. The documents listed in the left navigation teach you how to build Android apps using APIs in the Android framework and other libraries.

Trabajo Final para la cátedra Diseño de Sistemas de Tiempo Real. En este trabajo se muestra desde cómo armar el robot con las piezas compradas hasta cómo modificar y adaptar RTuinOS (un SO de Tiempo Real) para que funcione en nuestro Arduino.

AppML stands for Application Modeling Language.
AppML runs in any HTML page. No installation is required.
AppML is a tool for bringing data to HTML applications:

From objects
From files
From databases

Este libro está dirigido, principalmente, a Estudiantes y Docentes que quieren aprender a programar como forma de fortalecer sus capacidades cognoscitivas y así obtener un beneficio adicional de su computador para lograr un mejor provecho de sus estudios. Dada la orientación del libro respecto a programar para resolver problemas asociados a las Ciencias e Ingenierías, el requisito mínimo de matemáticas que hemos elegido para presentar el contenido del mismo se cubre, normalmente, en el tercer año del bachillerato. No obstante, el requisito no es obligatorio para leer el libro en su totalidad y adquirir los conocimientos de programación obviando el contenido matemático.

Cerner les concepts et technologies standards des SOA ;Opérer un découpage fonctionnel des applications ;Indiquer l’accostage en Urbanisation et SOA ;Distinguer les méthodes d’analyse SOA ;Tester la mise en œuvre SOA via des outils appropriés.

This two week assignment asks students to interpret and analyze the 1974 Arecibo Message sent by Drake and Sagan. Week 1 introduces the concepts behind the construction of the message and engages with a critical analysis of the architecture and the contents of the message. Week 2 asks students to develop software in a Jupyter Notebook (available for free from the Anaconda Python Distribution) to interpret messages that were similar to those produced by Drake and Sagan.

This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.

This is the GitHub repository for course CMP 414/765: Artificial Intelligence taught at The City University of New York, Lehman College, in Fall 2022

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

An introduction to the main techniques of Artifical Intelligence: state-space search methods, semantic networks, theorem-proving and production rule systems. Important applications of these techniques are presented. Students are expected to write programs exemplifying some of techniques taught, using the LISP lanuage.

Artificial Intelligence (AI) promises to offer tremendous opportunities for service excellence, creation of newer domain of employment avenues, and in the process augment industrial and economic growth for countries. The question, however, is India ready to embrace AI and pave for a new technology revolution? India’s history does indeed support this possibility. In the past, starting in the late 1980s, India successfully clasped the computer revolution bubble and took almost a decade to become an information technology (IT) leader in the world. Today, the Indian IT sector contributes significantly to the country’s GDP and is among the highest employment opportunity provider in the country. The journey of India as an IT major, however, has not been without its ups and downs.  

What is the current state of artificial intelligence (AI) in the world of scholarly communication? What impact does AI have on the practices and strategies of publishers, libraries, information technology companies, and researchers? What exactly is AI and what are those in the realm of scholarly communication actually thinking about it and doing with it?

This Charleston Briefing seeks to provide some answers to these very important questions, offering both general essays on AI and more specific essays on AI in scholarly publishing, academic libraries, and AI in information discovery and knowledge building. The essays will help publishers, librarians, and researchers better understand the actual impact of AI on libraries and publishing so that they can respond to the potentially transformative impact of AI in a measured and knowledgeable manner.

"Charleston Briefings: Trending Topics for Information Professionals" is a thought-provoking series of brief books concerning innovation in the sphere of libraries, publishing, and technology in scholarly communication. The briefings, growing out of the vital conversations characteristic of the Charleston Conference and Against the Grain, will offer valuable insights into the trends shaping our professional lives and the institutions in which we work.

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.

This course teaches simple reasoning techniques for complex phenomena: divide and conquer, dimensional analysis, extreme cases, continuity, scaling, successive approximation, balancing, cheap calculus, and symmetry. Applications are drawn from the physical and biological sciences, mathematics, and engineering. Examples include bird and machine flight, neuron biophysics, weather, prime numbers, and animal locomotion. Emphasis is on low-cost experiments to test ideas and on fostering curiosity about phenomena in the world.

This hands-on workshop explores the tool Atelier that was developed to support feedback in programming tutorials. Our teaching philosophy for programming is based on a tinkering approach, that is characterised by playful exploration, driven by curiosity. Students define from the start their own assignments, given only a set of ingredients to use. The role of a teacher is to provide starting points, explain the first steps to take, and to get students unstuck when necessary. This approach puts students in a very active position but is also very feedback intensive.To support giving feedback and to reduce inconsistencies in feedback given by teaching assistants (TAs), we developed a tool, Atelier, that allows to give comments on code and share this with the respective student, and also with TAs and teachers.The hands-on activity will start with an introduction, followed by an online tutorial lecture with some simple programming assignments. Participants will take the role of students as well as TAs and use Atelier to give and receive feedback. After two rounds of programming exercises, we will evaluate the tool with the participants and discuss its use and place in programming education.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.

Software testing gets a bad rap for being difficult, time-consuming, redundant, and above all – boring. But in fact, it is a proven way to ensure that your software will work flawlessly and can meet release schedules.

In a two-course series, we will teach you automated software testing in an inspiring way. We will show you that testing is not as daunting a task as you might think, and how automated testing will make you a better developer who programs excellent software.

This second course builds upon the first course’s material. It covers more advanced tools and techniques and their applications, now utilizing more than just JUnit. Key topics include Test-Driven Development, state-based and web testing, combinatorial testing, mutation testing, static analysis tools, and property-based testing.

This is a highly practical course. Throughout the lessons, you will test various programs by means of different techniques. By the end, you will be able to choose the best testing strategies for different projects.

6.270 is a hands-on, learn-by-doing class, in which participants design and build a robot that will play in a competition at the end of January. The goal for the students is to design a machine that will be able to navigate its way around the playing surface, recognize other opponents, and manipulate game objects. Unlike the machines in Design and Manufacturing I (2.007), 6.270 robots are totally autonomous, so once a round begins, there is no human intervention.
The goal of 6.270 is to teach students about robotic design by giving them the hardware, software, and information they need to design, build, and debug their own robot. The subject includes concepts and applications that are related to various MIT classes (e.g. 6.001, 6.002, 6.004, and 2.007), though there are no formal prerequisites for 6.270.