Courses tagged with "Nutrition" (421)
Animation is a compelling and effective form of expression; it engages viewers and makes difficult concepts easier to grasp. Today's animation industry creates films, special effects, and games with stunning visual detail and quality. This graduate class will investigate the algorithms that make these animations possible: keyframing, inverse kinematics, physical simulation, optimization, optimal control, motion capture, and data-driven methods. Our study will also reveal the shortcomings of these sophisticated tools. The students will propose improvements and explore new methods for computer animation in semester-long research projects. The course should appeal to both students with general interest in computer graphics and students interested in new applications of machine learning, robotics, biomechanics, physics, applied mathematics and scientific computing.
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 is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.
This course is a comprehensive introduction to control system synthesis in which the digital computer plays a major role, reinforced with hands-on laboratory experience. The course covers elements of real-time computer architecture; input-output interfaces and data converters; analysis and synthesis of sampled-data control systems using classical and modern (state-space) methods; analysis of trade-offs in control algorithms for computation speed and quantization effects. Laboratory projects emphasize practical digital servo interfacing and implementation problems with timing, noise, and nonlinear devices.
6.374 examines the device and circuit level optimization of digital building blocks. Topics covered include: MOS device models including Deep Sub-Micron effects; circuit design styles for logic, arithmetic and sequential blocks; estimation and minimization of energy consumption; interconnect models and parasitics; device sizing and logical effort; timing issues (clock skew and jitter) and active clock distribution techniques; memory architectures, circuits (sense amplifiers) and devices; testing of integrated circuits. The course employs extensive use of circuit layout and SPICE in design projects and software labs.
In this course we’ll explore complex analysis, complex dynamics, and some applications of these topics.
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course 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.
This Capstone MOOC gives Signature Track students who passed all previous MOOCs in the MoCCA Specialization “with Distinction” an opportunity to integrate and demonstrate the knowledge they've acquired across the three earlier content area MOOCs.
Cryptography is present in everyday life, from paying with a credit card to using the telephone. Learn all about making and breaking puzzles in computing.
6.728 is offered under the department's "Devices, Circuits, and Systems" concentration. The course covers concepts in elementary quantum mechanics and statistical physics, introduces applied quantum physics, and emphasizes an experimental basis for quantum mechanics. Concepts covered include: Schrodinger's equation applied to the free particle, tunneling, the harmonic oscillator, and hydrogen atom, variational methods, Fermi-Dirac, Bose-Einstein, and Boltzmann distribution functions, and simple models for metals, semiconductors, and devices such as electron microscopes, scanning tunneling microscope, thermonic emitters, atomic force microscope, and others.
This course provides a phenomenological approach to superconductivity, with emphasis on superconducting electronics. Topics include: electrodynamics of superconductors, London's model, flux quantization, Josephson Junctions, superconducting quantum devices, equivalent circuits, high-speed superconducting electronics, and quantized circuits for quantum computing. The course also provides an overview of type II superconductors, critical magnetic fields, pinning, the critical state model, superconducting materials, and microscopic theory of superconductivity.
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.
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.
Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!
The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.
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.
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