Development of programs containing a significant amount of knowledge about their application domain. Outline: brief review of relevant AI techniques; case studies from a number of application domains, chosen to illustrate principles of system development; discussion of technical issues encountered in building a system, including selection of knowledge representation, knowledge acquisition, etc.; and discussion of current and future research. Hands-on experience in building an expert system (term project).

This course is a core electrical engineering computer science subject at MIT. It introduces concepts and techniques relevant to the production of large software systems. Students are taught a programming method based on the recognition and description of useful abstractions. Topics include: modularity; specification; data abstraction; object modeling; design patterns; and testing. Several programming projects of varying size undertaken by students working individually and in groups.

Experimental investigations of speech processes. Topics: measurement of articulatory movements; measurements of pressures and airflows in speech production; computer-aided waveform analysis and spectral analysis of speech; synthesis of speech; perception and discrimination of speechlike sounds; speech prosody; models for speech recognition; speech disorders; and other topics. Recommended prerequisites: 6.002 or 18.03. Alternate years.

If you've been looking to learn how to code, we can help you get started. Here are 4.5 lessons on the basics and extra resources to keep you going. Lesson 1: Variables and Basic Data Types. Lesson 2: Working with Variables. Lesson 3: Arrays and Logic Statements. Lesson 4: Understanding Functions and Making a Guessing Game

Here's a quick run-down of how we teach coding here:

We explain new concepts using a talk-through, which is like a video but more interactive.Then you'll do a step-by-step challenge to practice that concept.Finally, you'll work on a project, where you can get more practice and be more creative with the skills you've learned.

Lisa-Joy Zgorski of NSF and Helen Hastings, a senior at Thomas Jefferson High School for Science and Technology in Alexandria, Va., interviewed David Ferrucci, an IBM Fellow and the principal investigator for Watson/Jeopardy! during his March 8, 2012, visit to NSF. Hastings, who sits on "TJ" student government, and is sometimes the only female in her advanced computation courses despite being part of a student body that has achieved near gender parity, took the lead and asked Ferrucci some questions of her own. She asked how his team devised a computer to become a Jeopardy! champion and moreover, how the underlying technology of language processing has broader implications for advancing computer science research with useful applications in the not-so-distant future. What is the cause for a standing-room-only gathering of NSF employees? The talk Ferrucci gave that afternoon, all about the research!

These projects are downloadable step-by-step guides, with explanations and color screenshots for students to follow.
Each project is a stand-alone activity, written to last for a single lesson, and will guide children to create a game or interactive project that demonstrates a real-world use of artificial intelligence and machine learning.

Deriving a symbolic description of the environment from an image. Understanding physics of image formation. Image analysis as an inversion problem. Binary image processing and filtering of images as preprocessing steps. Recovering shape, lightness, orientation, and motion. Using constraints to reduce the ambiguity. Photometric stereo and extended Gaussian sphere. Applications to robotics; intelligent interaction of machines with their environment. Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.

This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

The focus of the course is on medical science and practice in the age of automation and the genome, both present and future. It includes an analysis of the computational needs of clinical medicine, a review systems and approaches that have been used to support those needs, and an examination of new technologies.

Presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. Emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Technical focus on decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. Students produce a final project using the methods learned in the subject, based on actual clinical data. (Required for students in the Master's Program in Medical Informatics, but open to other graduate students and advanced undergraduates.)

This free ESL lesson plan on the metaverse has been designed for adults and young adults at an intermediate (B1/B2) to advanced (C1/C2) level and should last around 45 to 60 minutes for one student.

Modeling of microelectronic devices, and basic microelectronic circuit analysis and design. Physical electronics of semiconductor junction and MOS devices. Relation of electrical behavior to internal physical processes; development of circuit models; and understanding the uses and limitations of various models. Use of incremental and large-signal techniques to analyze and design bipolar and field effect transistor circuits, with examples chosen from digital circuits, single-ended and differential linear amplifiers, and other integrated circuits. Design project. Description from the course home page: 6.012 is the header course for the department's "Devices, Circuits and Systems" concentration. The topics covered include: modeling of microelectronic devices, basic microelectronic circuit analysis and design, physical electronics of semiconductor junction and MOS devices, relation of electrical behavior to internal physical processes, development of circuit models, and understanding the uses and limitations of various models. The course uses incremental and large-signal techniques to analyze and design bipolar and field effect transistor circuits, with examples chosen from digital circuits, single-ended and differential linear amplifiers, and other integrated circuits.

MASLab (Mobile Autonomous System Laboratory) is a robotics contest. The contest takes place during MIT's Independent Activities Period and participants earn 6 units of P/F credit and 6 Engineering Design Points. Teams of three to four students have less than a month to build and program sophisticated robots which must explore an unknown playing field and perform a series of tasks. MASLab provides a significantly more difficult robotics problem than many other university-level robotics contests. Although students know the general size, shape, and color of the floors and walls, the students do not know the exact layout of the playing field. In addition, MASLab robots are completely autonomous, or in other words, the robots operate, calculate, and plan without human intervention. Finally, MASLab is one of the few robotics contests in the country to use a vision based robotics problem.

Languages and compilers to exploit multithreaded parallelism. Implicit parallel programming using functional languages and their extensions. Higher-order functions, non-strictness, and polymorphism. Explicit parallel programming and nondeterminism. The lambda calculus and its variants. Term rewriting and operational semantics. Compiling multithreaded code for symmetric multiprocessors and clusters. Static analysis and compiler optimizations.

Computer-aided design methodologies for synthesis of multivariable feedback control systems. Performance and robustness trade-offs. Model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; nonlinear effects. Computer-aided (MATLAB) design homework using models of physical processes. This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLABĺ¨) design problems.

Relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of the analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; augmented transition network grammars. Question answering systems. Extensive laboratory work on building natural language processing systems. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

This course is a graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, and finance, as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. We will also consider other extensions of network flow problems.

This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton's method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.

This site includes link to a free online code creator.  There are also introductory videos and tutorials that relate.  It requires a log-in, but has a good mix of code and visuals to keep the new code learner interested.