But not to those at OCW.Gil Strang teaches 18.06 (Matrix Methods in Data Analysis, Signal Processing, and Machine Learning). The videos are garnering praise and thanks from viewers around the world. Each month, OCW publishes a list of its most-visited courses, and Newton points out that Strang’s course has always been among the top 10 most-viewed since OCW launched.
1. 4. Fourth Edition : Brooks/Cole/Cengage (2006). The 18.06 class soon became popular with science and engineering students, at MIT and around the world. The 18.06 site has received more than 3 million visits since its first publication in 2002. Strang has a quick smile and an encouraging manner. “He cracked the 10 million number,” he says. “In the new version, they do almost no lecturing ... and one reason they feel that they can get away with that is that they can send students to Gil’s lectures on OCW.”Strang, the MathWorks Professor of Mathematics, received his BS from MIT in 1955. Now in its fifth edition, Strang’s textbook "Introduction to Linear Algebra" has been translated into French, German, Greek, Japanese, and Portuguese. “A big part of my life is to open mathematics to students everywhere,” says Strang. “I'm very supportive of the whole idea of making these courses available to people around the world. Gilbert Strang gs@math.mit.edu Wellesley-Cambridge Press (for ordering information) Book Order Form ... 4.4 Convolution and Signal Processing 4.5 Fourier Integrals 4.6 Deconvolution and Integral Equations 4.7 Wavelets and Signal Processing 5 Analytic Functions Gil Strang teaches 18.06 (Matrix Methods in Data Analysis, Signal Processing, and Machine Learning). There are about 2,450 courses on OCW currently, with over 100 having complete video lectures, and more going up as fast as OCW can post them.“Professor Strang structures the class so that ideas seem to flow from the students into proofs,” says senior and math major Jesse Michel.
Open thinking has played a major role in his professional career. In the class and book, Strang starts with linear algebra and moves to optimization by gradient descent, and then to the structure and analysis of deep learning. Even the book’s cover is evocative. Exams are outlawed. Professor Strang’s energy and emphasis on the exciting points keeps the class on the edge of their seats.”After a lifetime of teaching at MIT, he is still able to project energy and enthusiasm over his subject. “I’m not teaching the math guys who jump over linear algebra,” he says. Introduction to Applied Mathematics, Wellesley-Cambridge Press (1986). His goal is to organize central methods and ideas of data science, and to show how the language of linear algebra expresses those ideas.“This was linear algebra for signals and data, and it was alive,” says Strang. The videos don’t feature fancy graphics or music, but are an homage to the power of old-school lectures with a chalkboard by a master teacher.“This lecture series is one of the few that I like to watch for fun,” says one commenter.