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Stanford Engineering Everywhere (SEE)
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Stanford University is offering 10 free online courses in the fields of Computer Science and Electrical Engineering. This includes video lectures, handouts, assignments, and exams all free to download with no registration required. This really is a great opportunity for anyone thinking of pursuing a career in these fields!

For the first time in its history, Stanford is offering some of its most popular engineering classes free of charge to students and educators around the world. Stanford Engineering Everywhere (SEE) expands the Stanford experience to students and educators online. A computer and an Internet connection is all you need. View lecture videos, access reading lists and other course handouts, take quizzes and tests, and communicate with other SEE students, all at your convenience.
-Stanford School of Engineering

The courses available include..

Introduction to Computer Science
-Programming Methodology CS106A
-Programming Abstractions CS106B
-Programming Paradigms CS107

Artificial Intelligence
-Introduction to Robotics CS223A
-Natural Language Processing CS224N
-Machine Learning CS229

Linear Systems and Optimization
-The Fourier Transform and its Applications EE261
-Introduction to Linear Dynamical Systems EE263
-Convex Optimization I EE364A
-Convex Optimization II EE364B


In depth course details..

Introduction to Computer Science

Programming Methodology

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.

Programming Abstractions

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.

Programming Paradigms:

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#.

Artificial Intelligence

Introduction to Robotics

Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design.

Natural Language Processing (no video for this one) (Correction: video can’t be downloaded but it is there on the site. Thanks, Christopher!)

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.

Machine Learning

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.

Linear Systems and Optimization

The Fourier Transform and its Applications

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.

Introduction to Linear Dynamical 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.

Convex Optimization I

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.

Convex Optimization II

Continuation of Convex Optimization I. 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. Course requirements include a substantial project.

-Moue Magazine »Stanford Offers Free Online Courses in CS, Robotics

Source: Stanford Engineering Everywhere