Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

This is part of MIT’s OpenCourseWare website. It includes a course syllabus, reading list, lecture videos, Powerpoint slides and code, in-class questions and video questions as well as assignments – all available for download. The course 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 syllabus contains information, websites, and resources that are freely available to students as an alternative to a single textbook that is purchased. The semester course focuses on two major sections: 1) Learning Microsoft Office 2019 and 2) Computer Concepts. Students should develop a comfortable understanding of working in Microsoft Office 2019 as well as gain knowledge of computer concepts after taking this course.

This is a seminar series led by graduate students and postdocs in the MIT Department of Brain and Cognitive Sciences (BCS) from 2015 to the present, featuring tutorials on computational topics relevant to research on intelligence in neuroscience, cognitive science, and artificial intelligence. These tutorials are aimed at participants who have some computational background but are not experts on these topics.
A computational tutorial can consist of any method, tool, or model that is broadly relevant within neuroscience, cognitive science, and artificial intelligence. The goal is to bring researchers in brain and cognitive sciences closer to the researchers creating computational methods. 
Resources posted here include lecture videos, lecture slides, code and datasets for exercises, background references, and other supplementary material. Typically, each tutorial consists of a short lecture, and an interactive part with tutorials or "office hours" to work through practice problems and discuss how the material may be applied to participants’ research. 
This series was organized by Emily Mackevicius, Jenelle Feather, Nhat Le, Fernanda De La Torre Romo, and Greta Tuckute, with financial support from BCS. Videos were filmed, edited, and produced by Kris Brewer, Director of Technology at the Center for Brains, Minds, and Machines (CBMM).

The projects in this guide use a student-driven approach to learning. Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from machine learning models to video games—to demonstrate their learning.

Project 1: Programming with Machine Learning
Project 2: AI-Powered Players in Video Games
Project 3: Using AI for Robotic Motion Planning
Project 4: Machine Learning as a Service

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education

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.

Foundations of Computation is a free textbook for a one-semester course in theoretical computer science. It has been used for several years in a course at Hobart and William Smith Colleges. The course has no prerequisites other than introductory computer programming. The first half of the course covers material on logic, sets, and functions that would often be taught in a course in discrete mathematics. The second part covers material on automata, formal languages, and grammar that would ordinarily be encountered in an upper level course in theoretical computer science.

Freebookcentre.net's computer science section contains links to many technical books offered free online, either as html pages or downloadable pdfs. Books are arranged by subject: Data Structures and Algorithms, Compiler Design, Object Oriented Programming, Operating Systems, Computation Theory, Artificial Intelligence, and others.

Homework for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Homework for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

This course focuses on one particular aspect of the history of computing: the use of the computer as a scientific instrument. The electronic digital computer was invented to do science, and its applications range from physics to mathematics to biology to the humanities. What has been the impact of computing on the practice of science? Is the computer different from other scientific instruments? Is computer simulation a valid form of scientific experiment? Can computer models be viewed as surrogate theories? How does the computer change the way scientists approach the notions of proof, expertise, and discovery? No comprehensive history of scientific computing has yet been written. This seminar examines scientific articles, participants’ memoirs, and works by historians, sociologists, and anthropologists of science to provide multiple perspectives on the use of computers in diverse fields of physical, biological, and social sciences and the humanities. We explore how the computer transformed scientific practice, and how the culture of computing was influenced, in turn, by scientific applications.

Computability Theory deals with one of the most fundamental questions in computer science: What is computing and what are the limits of what a computer can compute? Or, formulated differently: ‰"What kind of problems can be algorithmically solved?‰" During the course this question will be studied. Firstly, the notion of algorithm or computing will be made precise by using the mathematical model of a Turing machine. Secondly, it will be shown that basic issues in computer science, like "Given a program P does it halt for any input x?" or "Given two program P and Q, are they equivalent?" cannot be solved by any Turing machine. This shows that there exist problems that are impossible to solve with a computer, the so-called "undecidable problems".

Homework for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Homework for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

For this 3-part project, students will practice using the problem-solving steps by pretending to help a family member or friend who has asked them to give a recommendation of which computer to buy.

The purpose of this course is to cultivate an understanding of modern computing technology through an in-depth study of the interface between hardware and software. The student will study the history of modern computing technology before learning about modern computer architecture, then the recent switch from sequential processing to parallel processing. Upon completion of this course, students will be able to: identify important advances that have taken place in the history of modern computing and discuss some of the latest trends in computing industry; explain how programs written in high-level programming language, such as C or Java, can be translated into the language of the hardware; describe the interface between hardware and software and explain how software instructs hardware to accomplish desired functions; demonstrate an understanding of the process of carrying out sequential logic design; demonstrate an understanding of computer arithmetic hardware blocks and floating point representation; explain how a hardware programming language is executed on hardware and how hardware and software design affect performance; demonstrate an understanding of the factors that determine the performance of a program; demonstrate an understanding of the techniques that designers use to improve the performance of programs running on hardware; demonstrate an understanding of the importance of memory hierarchy in computer design and explain how memory design impacts overall hardware performance; demonstrate an understanding of storage and I/O devices, their performance measurement, and redundant array of inexpensive disks (more commonly referred to by the acronym RAID) technology; list the reasons for and the consequences of the recent switch from sequential processing to parallel processing in hardware manufacture and explain the basics of parallel programming. (Computer Science 301)

Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.