Shai Shalev - Shwartz Scribe : Shai Shalev - Shwartz
نویسنده
چکیده
The subject of this course is automated learning, or, as we will more often use, machine learning (ML for short). Roughly speaking, we wish to program computers so that they can ”learn”. Before we discuss how machines can learn, or how the process of learning can be automated, let us consider two examples of naturally occurring animal learning. Not surprisingly, some of the most fundamental issues in ML arise already in that context, that we are all familiar with.
منابع مشابه
Faster Low-rank Approximation using Adaptive Gap-based Preconditioning
We propose a method for rank k approximation to a given input matrix X P R which runs in time
متن کاملQuantity Makes Quality: Learning with Partial Views
In many real world applications, the number of examples to learn from is plentiful, but we can only obtain limited information on each individual example. We study the possibilities of efficient, provably correct, large-scale learning in such settings. The main theme we would like to establish is that large amounts of examples can compensate for the lack of full information on each individual e...
متن کاملStrongly Adaptive Online Learning
Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms to strongly adaptive. As a consequence, we derive simple, yet efficient, strongly adaptive algorithms for a handful of problems.
متن کاملThe Kernelized Stochastic Batch Perceptron
We present a novel approach for training kernel Support Vector Machines, establish learning runtime guarantees for our method that are better then those of any other known kernelized SVM optimization approach, and show that our method works well in practice compared to existing alternatives.
متن کامل