These are lecture notes that are based on the lectures from a class I taught on the topic of Randomized Linear Algebra (RLA) at UC Berkeley during the Fall 2013 semester.

Manifold learning and dimensionality reduction techniques are ubiquitous in science and engineering, but can be computationally expensive procedures when applied to large data sets or when similarities are expensive to compute. To date, little work has been done to investigate the tradeoff between computational resources and the quality of learned representations. We present both theoretical and experimental explorations of this question. In particular, we consider Laplacian eigenmaps embeddings based on a kernel matrix, and explore how the embeddings behave when this kernel matrix is corrupted by occlusion and noise. Our main theoretical result shows that under modest noise and occlusion assumptions, we can (with high probability) recover a good approximation to the Laplacian eigenmaps embedding based on the uncorrupted kernel matrix. Our results also show how regularization can aid this approximation. Experimentally, we explore the effects of noise and occlusion on Laplacian eigenmaps embeddings of two real-world data sets, one from speech processing and one from neuroscience, as well as a synthetic data set.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Back before there were popular toastsharing apps like "Crusti" and hook-up sites for bakers like "Hotbred," people used to have machines in their kitchens that made bread into toast. It fulfilled the function of an appliance, which was to make some aspect of life easier. No more people standing over slices of bread with a blowtorch; this contraption did it for us. Presumably, millions of person-hours were saved by letting the toaster do this crucial work.

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An earlier version of this paper was presented on Oct. 21, 1984 as The Thoughts of Armchairs and Children at Middle Atlantic States Philosophy of Education Society Fall Conference, Rutgers University "Thinking" Like Computers Do 2004 Edward G. Rozycki, Ed. But what is then the difference between the internal speech of this armchair and that of another standing by it? How is it then with a human being? Where does she talk to herself? Why is it that this question seems senseless -- no specification of place is necessary except to say that the human is talking to herself? On the other hand the question as to where the armchair is talking to itself seems to demand such an answer. The reason is this: we want to know how the armchair is supposed to be like a human being, whether, for example, its head is the upper back of the chair, etc. What process is occurring when one talks to oneself inwardly?

With the corporate world embracing digital technologies, organisations are increasingly looking for resources with a difference who could enable them to create a distinctive presence in the marketplace. They are keen that the resources they onboard have analytical skills, visioning skills, learning skills, innovation capabilities, social skills and problem solving skills at all levels in the organisation--not just at the top. This is because digital transformation is unleashing a virtuous change at a regular interval to the processes and interactions with various stakeholders at multiple levels within the organisation and within the industry and it is expected of employees to adapt and respond quickly to these changes. Hence in addition to reorienting the employees within the organisation towards the need for the skills mentioned above, the new generation employees organisations wish to employ are expected to have these competencies. The mindset and the capabilities required to be successful in the digital era have to be developed while the next generation is still part of the academic system.

Description: Engineers make predictions for their technologies in 2020: Computers that understand your emotions and refrigerators that can communicate with the rest of your home without batteries.

Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker's state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker is the first neural-network tracker that learns to track generic objects at 100 fps.

Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. The field of educational technology is mourning a visionary whose work was considered 50 years ahead of its time. Seymour Papert, who died July 31 at age 88, was a mathematician and computer scientist who spent decades at MIT. "Seymour was one of the very first people to recognize that new computer technologies could be used by kids to create things in new ways and express themselves," Mitchel Resnick, a professor of learning research at MIT and a longtime colleague and friend, told NPR Ed. "It's amazing that Seymour was thinking these ideas in the 1960s," Resnick adds, "when computers cost hundreds of thousands of dollars, but he foresaw the day that every child would have access to a computer." The great theme of Papert's work and life was the nature of intelligence, or what he called thinking about thinking.