# Online courses directory (14)

How have humans protected their secret messages through history? What has changed today?. What is Cryptography?. Probability Space. The Caesar Cipher. Caesar Cipher Exploration. Frequency Fingerprint Exploration . Polyalphabetic Cipher. Polyalphabetic Exploration. The One-Time Pad. Perfect Secrecy Exploration. Frequency Stability. Coin flip sequences. Frequency Stability Exploration. The Enigma Encryption Machine (case study). Perfect Secrecy. Pseudorandom Number Generators. Random Walk Exploration. Ciphers vs. Codes. Shift Cipher. Caesar cipher encryption. Caesar Cipher Decryption. Caesar cipher frequency analysis. Vigenere cipher encryption. XOR Bitwise Operation. XOR & the One-Time Pad. XOR Exploration. Bitwise Operators. What's Next?. The Fundamental Theorem of Arithmetic. Public Key Cryptography: what is it?. The Discrete Logarithm Problem. Diffie-Hellman Key Exchange. RSA Encryption: step 1. RSA Encryption: step 2. RSA Encryption: step 3. Time Complexity (Exploration). Euler's Totient Function. Euler Totient Exploration. RSA Encryption: step 4. What should we learn next?. What is Modular Arithmetic?. Modulo Operator. Congruence Modulo. Congruence Relation. Equivalence Relations. The Quotient Remainder Theorem. Modular Addition & Subtraction. Modular Addition. Modular Multiplication. Modular Multiplication. Modular Exponentiation. Fast Modular Exponentiation. Fast Modular Exponentiation. Modular Inverses. Introduction. Primality Test Challenge. Trial Division. Level 1: Primality Test. Running Time. Level 2: measuring running time. Computer Memory (space). Binary Memory Exploration. Algorithmic Efficiency. Level 3: Challenge. Sieve of Eratosthenes. Level 4: Sieve of Eratosthenes. Primality Test with Sieve. Level 5: Trial division using sieve. The Prime Number Theorem. Prime density spiral. Prime Gaps. Time Space Tradeoff. Summary (what's next?). Randomized Algorithms (intro). Conditional Probability (Bayes Theorem) Visualized. Guess the coin. Random Primality Test (warm up). Level 9: Trial Divison vs Random Division. Fermat's Little Theorem. Fermat Primality Test. Level 10: Fermat Primality Test. What's Next?. What is Cryptography?. Probability Space. The Caesar Cipher. Caesar Cipher Exploration. Frequency Fingerprint Exploration . Polyalphabetic Cipher. Polyalphabetic Exploration. The One-Time Pad. Perfect Secrecy Exploration. Frequency Stability. Coin flip sequences. Frequency Stability Exploration. The Enigma Encryption Machine (case study). Perfect Secrecy. Pseudorandom Number Generators. Random Walk Exploration. Ciphers vs. Codes. Shift Cipher. Caesar cipher encryption. Caesar Cipher Decryption. Caesar cipher frequency analysis. Vigenere cipher encryption. XOR Bitwise Operation. XOR & the One-Time Pad. XOR Exploration. Bitwise Operators. What's Next?. The Fundamental Theorem of Arithmetic. Public Key Cryptography: what is it?. The Discrete Logarithm Problem. Diffie-Hellman Key Exchange. RSA Encryption: step 1. RSA Encryption: step 2. RSA Encryption: step 3. Time Complexity (Exploration). Euler's Totient Function. Euler Totient Exploration. RSA Encryption: step 4. What should we learn next?. What is Modular Arithmetic?. Modulo Operator. Congruence Modulo. Congruence Relation. Equivalence Relations. The Quotient Remainder Theorem. Modular Addition & Subtraction. Modular Addition. Modular Multiplication. Modular Multiplication. Modular Exponentiation. Fast Modular Exponentiation. Fast Modular Exponentiation. Modular Inverses. Introduction. Primality Test Challenge. Trial Division. Level 1: Primality Test. Running Time. Level 2: measuring running time. Computer Memory (space). Binary Memory Exploration. Algorithmic Efficiency. Level 3: Challenge. Sieve of Eratosthenes. Level 4: Sieve of Eratosthenes. Primality Test with Sieve. Level 5: Trial division using sieve. The Prime Number Theorem. Prime density spiral. Prime Gaps. Time Space Tradeoff. Summary (what's next?). Randomized Algorithms (intro). Conditional Probability (Bayes Theorem) Visualized. Guess the coin. Random Primality Test (warm up). Level 9: Trial Divison vs Random Division. Fermat's Little Theorem. Fermat Primality Test. Level 10: Fermat Primality Test. What's Next?.

We've always been communicating.... as we moved from signal fires, to alphabets & electricity the problems remained the same. What is Information Theory?. Prehistory: Proto-writing. Ptolemaic: Rosetta Stone. Ancient History: The Alphabet. Source Encoding. Visual Telegraphs (case study). Decision Tree Exploration. Electrostatic Telegraphs (case study). The Battery & Electromagnetism. Morse Code & The Information Age. Morse code Exploration. What's Next?. Symbol Rate. Symbol Rate Exploration. Introduction to Channel Capacity. Message Space Exploration. Measuring Information. Galton Board Exploration. Origin of Markov Chains. Markov Chain Exploration. A Mathematical Theory of Communication. Markov Text Exploration. What's Next?. What is Information Theory?. Prehistory: Proto-writing. Ptolemaic: Rosetta Stone. Ancient History: The Alphabet. Source Encoding. Visual Telegraphs (case study). Decision Tree Exploration. Electrostatic Telegraphs (case study). The Battery & Electromagnetism. Morse Code & The Information Age. Morse code Exploration. What's Next?. Symbol Rate. Symbol Rate Exploration. Introduction to Channel Capacity. Message Space Exploration. Measuring Information. Galton Board Exploration. Origin of Markov Chains. Markov Chain Exploration. A Mathematical Theory of Communication. Markov Text Exploration. What's Next?.

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.

This course will introduce you to the field of computer science and the fundamentals of computer programming. Introduction to Computer Science I is specifically designed for students with no prior programming experience, and taking this course does not require a background in Computer Science. This course will touch upon a variety of fundamental topics within the field of Computer Science and will use Java, a high-level, portable, and well-constructed computer programming language developed by Sun Microsystems (now Oracle), to demonstrate those principles. We will begin with an overview of the course topics as well as a brief history of software development. We will cover basic object-oriented programming terminology and concepts such as objects, classes, inheritance, and polymorphism, as well as the fundamentals of Java, its primitive data types, relational operators, control statements, exception handling, and file input /output. By the end of the course, you should have a strong understanding of the fundam…

This course will introduce you to modern operating systems. We will focus on UNIX-based operating systems, though we will also learn about alternative operating systems, including Windows. The course will begin with an overview of the structure of modern operating systems. Over the course of the subsequent units, we will discuss the history of modern computers, analyze in detail each of the major components of an operating system (from processes to threads), and explore more advanced topics in the field, including memory management and file input/output. The class will conclude with a discussion of various system-related security issues.

Though we may not recognize them in our everyday activities, databases are everywhere. They are hidden behind your online banking profile, airline reservation systems, medical records, and even employment records. This course will provide students with a general overview of databases, introducing you to database history, modern database systems, the different models used to design a database, and Structured Query Language (SQL), which is the standard language used to access and manipulate databases. Many of the principles of database systems carry to other areas in computer science, especially operating systems. Databases are often thought of as one of the core computer science topics, since many other areas in the discipline have been derived from this area.

This class covers the history of 20th century art and design from the perspective of the technologist. Methods for visual analysis, oral critique, and digital expression are introduced. Class projects this term use the OLPC XO (One Laptop Per Child) laptop, Csound and Python software.

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. ***Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!***

This course explores the history of private and public rights in scientific discoveries and applied engineering, leading to the development of worldwide patent systems. The classes of invention protectable under the patent laws of the U.S., including the procedures in protecting inventions in the Patent Office and the courts will be examined. A review of past cases involving inventions and patents in:

- the chemical process industry and medical pharmaceutical, biological, and genetic-engineering fields;
- devices in the mechanical, ocean exploration, civil, and/or aeronautical fields;
- the electrical, computer, software, and electronic areas, including key radio, solid-state, computer and software inventions; and also
- software protection afforded under copyright laws.

Periodic joint real-time class sessions and discussions by video-audio Internet conferencing, with other universities will also be conducted.

This course begins with an introduction to the theory of computability, then proceeds to a detailed study of its most illustrious result: Kurt Gödel's theorem that, for any system of true arithmetical statements we might propose as an axiomatic basis for proving truths of arithmetic, there will be some arithmetical statements that we can recognize as true even though they don't follow from the system of axioms. In my opinion, which is widely shared, this is the most important single result in the entire history of logic, important not only on its own right but for the many applications of the technique by which it's proved. We'll discuss some of these applications, among them: Church's theorem that there is no algorithm for deciding when a formula is valid in the predicate calculus; Tarski's theorem that the set of true sentence of a language isn't definable within that language; and Gödel's second incompleteness theorem, which says that no consistent system of axioms can prove its own consistency.

This course introduces students to the ideas and practices surrounding teaching, learning and research at a world class research university like the University of Michigan, and the emerging role in these practices of Open Educational Resources, including open content such as opencourseware, open access initiatives, open publishing of research and learning materials as found in open journals, databases and e-prints, open textbooks, related open software efforts such as open learning systems, and emerging open teaching experiments. The course will ground the students in how teaching, learning and research is done at the university level, and then survey relevant OER efforts, looking at their history, development, potential futures, and the underlying motivations for their progressive adoption by various members of the community of scholars. more... This course uses an open textbook Open Educational Resources at the University of Michigan. The articles in the open textbook (wikibook) were written by the School of Information Graduate students in the class. Course Level: Graduate This Work, SI 521 - Special Topics: Open Educational Resources and the University of Michigan, by Joseph Hardin is licensed under a Creative Commons Attribution license.

This course focuses on the current state of "digital libraries" from a multidisciplinary perspective. Its point of departure is the possibilities and prospects for convergence of professions and cultures around the notion of digital media and content. The course covers the history of the idea of the digital library and the digital archive, especially its manifestation as projects and programs in academic, nonprofit, and research settings, and the suite of policy issues that influence the development and growth of digital libraries and archives. A foundation of core archival principles as applied in digital library and archives settings serves as an intellectual construct supporting the exploration of the related concepts of scholarly communication, digital preservation, cyberinfrastructure, representation, and standards/best practices. Students are expected to master a diverse literature, to participate actively in the discussion of issues, and to take steps, collectively and individually, to advance our understanding of future directions of digital libraries and archives. Course Level: Graduate This Work, SI 640 - Digital Libraries and Archives, by Paul Conway is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike license.