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4434Artificial Intelligence: Introduction to Robotics
http://www.merlot.org/merlot/viewMaterial.htm?id=351548
Introduction to Robotics is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Artificial Intelligence series and is taught by Professor Oussama Khatib of Stanford University's Computer Science Department. The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.The course is presented in a standard format of lectures, readings and problem sets. Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design.Artificial Intelligence: Machine Learning
http://www.merlot.org/merlot/viewMaterial.htm?id=351579
Machine Learning is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Artificial Intelligence series and is taught by Andrew Ng, Assistant Professor of Stanford University's Computer Science Department. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.Introduction to Computer Science: Programming Methodology
http://www.merlot.org/merlot/viewMaterial.htm?id=350166
Programming Methodology is one of ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Introduction to Computer Science series and is taught by Mehran Sahami, Associate Professor of Stanford University's Computer Science Department.This course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widely-used Java programming language along with good software engineering principles. Emphasis is on good programming style and the built-in facilities of the Java language. The course is explicitly designed to appeal to humanists and social scientists as well as hard-core techies. In fact, most Programming Methodology graduates end up majoring outside of the School of Engineering. Prerequisites: The course requires no previous background in programming, but does require considerable dedication and hard work.Artificial Intelligence: Natural Language Processing
http://www.merlot.org/merlot/viewMaterial.htm?id=351552
Natural Language Processing is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Artificial Intelligence series and is taught by Christopher Manning, Associate Professor of Stanford University's Computer Science Department. This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.Introduction to Computer Science: Programming Abstractions
http://www.merlot.org/merlot/viewMaterial.htm?id=351399
Programming Abstractions is one of ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Introduction to Computer Science series and is taught by Julie Zelenski of Stanford University's Computer Science Department.This course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming language, which is similar to both C and Java. If you've taken the Computer Science AP exam and done well (scored 4 or 5) or earned a good grade in a college course, Programming Abstractions may be an appropriate course for you to start with, but often Programming Abstractions (Accelerated) is a better choice. Programming Abstractions assumes that you already have familiarity with good programming style and software engineering issues (at the level of Programming Methodology), and that you can use this understanding as a foundation on which to tackle new topics in programming and data abstraction. Topics: Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities.Introduction to Computer Science: Programming Paradigms
http://www.merlot.org/merlot/viewMaterial.htm?id=351538
Programming Paradigms is one of ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Introduction to Computer Science series and is taught by Jerry Cain of Stanford University's Computer Science Department. Programming Paradigms topics include advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms. The functional paradigm (using LISP) and concurrent programming (using C and C++). Brief survey of other modern languages such as Python, Objective C, and C#.Linear Systems and Optimization | Introduction to Linear Dynamical Systems
http://www.merlot.org/merlot/viewMaterial.htm?id=351750
Introduction to Linear Dynamical Systems is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Linear Systems and Optimization series and is taught by Professor Stephen Boyd of Stanford University's Electrical Engineering Department. The course covers introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems.Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Eigenvalues, left and right eigenvectors, and dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions. Control, reachability, state transfer, and least-norm inputs. Observability and least-squares state estimation.Linear Systems and Optimization: Convex Optimization I
http://www.merlot.org/merlot/viewMaterial.htm?id=351757
Convex Optimization I is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Linear Systems and Optimization series and is taught by Professor Stephen Boyd of Stanford University's Electrical Engineering Department. The course concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.Linear Systems and Optimization: Convex Optimization II
http://www.merlot.org/merlot/viewMaterial.htm?id=351764
Convex Optimization II is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Linear Systems and Optimization series and is taught by Professor Stephen Boyd of Stanford University's Electrical Engineering Department. The course covers subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications.Linear Systems and Optimization: The Fourier Transform and its Applications
http://www.merlot.org/merlot/viewMaterial.htm?id=351595
The Fourier Transform and its Applications is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Linear Systems and Optimization series and is taught by Professor Brad Osgood, of Stanford University's Electrical Engineering Department. The goals for the course are to gain a facility with using the Fourier transform, both specific techniques and general principles, and learning to recognize when, why, and how it is used. Together with a great variety, the subject also has a great coherence, and the hope is students come to appreciate both. Topics include: The Fourier transform as a tool for solving physical problems. Fourier series, the Fourier transform of continuous and discrete signals and its properties. The Dirac delta, distributions, and generalized transforms. Convolutions and correlations and applications; probability distributions, sampling theory, filters, and analysis of linear systems. The discrete Fourier transform and the FFT algorithm. Multidimensional Fourier transform and use in imaging. Further applications to optics, crystallography. Emphasis is on relating the theoretical principles to solving practical engineering and science problems.