Online courses directory (684)

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.

The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.

This is a foundation subject in modern software development techniques for engineering and information technology. The design and development of component-based software (using C# and .NET) is covered; data structures and algorithms for modeling, analysis, and visualization; basic problem-solving techniques; web services; and the management and maintenance of software. Includes a treatment of topics such as sorting and searching algorithms; and numerical simulation techniques. Foundation for in-depth exploration of image processing, computational geometry, finite element methods, network methods and e-business applications. This course is a core requirement for the Information Technology M. Eng. program.

This class was also offered in Course 13 (Department of Ocean Engineering) as 13.470J. In 2005, ocean engineering subjects became part of Course 2 (Department of Mechanical Engineering), and the 13.470J designation was dropped in lieu of 2.159J.

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

Ever played the Kevin Bacon game? This class will show you how it works by giving you an introduction to the design and analysis of algorithms, enabling you to discover how individuals are connected.

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.

In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

In this course you will learn several fundamental principles of advanced algorithm design: greedy algorithms and applications; dynamic programming and applications; NP-completeness and what it means for the algorithm designer; the design and analysis of heuristics; and more.

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. Part I covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

Why do Primes make some problems fundamentally hard? Build algorithms to find out!. Primality Test. Running Time. Computer Memory (space). Algorithmic Efficiency. Sieve of Eratosthenes. Primality Test with Sieve. The Prime Number Theorem. Time Space Tradeoff. Conditional Probability Visualized.

Explore how we have hidden secret messages through history. What is Cryptography?. Probability Space. The Caesar Cipher. Polyalphabetic Cipher. The One-Time Pad. Frequency Stability. The Enigma Encryption Machine (case study). Perfect Secrecy. Pseudorandom Number Generators.

Find out how modern electronic markets work, why stock prices change in the ways they do, and how computation can help our understanding of them.  Build algorithms and visualizations to inform investing practice.

Introduction to programming and computer science. Introduction to Programs Data Types and Variables. Binary Numbers. Python Lists. For Loops in Python. While Loops in Python. Fun with Strings. Writing a Simple Factorial Program. (Python 2). Stepping Through the Factorial Program. Flowchart for the Factorial Program. Python 3 Not Backwards Compatible with Python 2. Defining a Factorial Function. Diagramming What Happens with a Function Call. Recursive Factorial Function. Comparing Iterative and Recursive Factorial Functions. Exercise - Write a Fibonacci Function. Iterative Fibonacci Function Example. Stepping Through Iterative Fibonacci Function. Recursive Fibonacci Example. Stepping Through Recursive Fibonacci Function. Exercise - Write a Sorting Function. Insertion Sort Algorithm. Insertion Sort in Python. Stepping Through Insertion Sort Function. Simpler Insertion Sort Function. Introduction to Programs Data Types and Variables. Binary Numbers. Python Lists. For Loops in Python. While Loops in Python. Fun with Strings. Writing a Simple Factorial Program. (Python 2). Stepping Through the Factorial Program. Flowchart for the Factorial Program. Python 3 Not Backwards Compatible with Python 2. Defining a Factorial Function. Diagramming What Happens with a Function Call. Recursive Factorial Function. Comparing Iterative and Recursive Factorial Functions. Exercise - Write a Fibonacci Function. Iterative Fibonacci Function Example. Stepping Through Iterative Fibonacci Function. Recursive Fibonacci Example. Stepping Through Recursive Fibonacci Function. Exercise - Write a Sorting Function. Insertion Sort Algorithm. Insertion Sort in Python. Stepping Through Insertion Sort Function. Simpler Insertion Sort Function.

This course delivers a systematic overview of computer vision, emphasizing two key issues in modeling vision: space and meaning. We will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene.

In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities and more.

In this class you will look behind the scenes of image and video processing, from the basic and classical tools to the most modern and advanced algorithms.

This class teaches you about basic concepts in theoretical computer science -- such as NP-completeness -- and what they imply for solving tough algorithmic problems.

Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems.