Many problems in real-world applications of machine learning can be formalized as classical statistical problems, e.g., pattern recognition, regression or dimension reduction, with the caveat that the data are often not vectors of numbers. For example, protein sequences and structures in computational biology, text and XML documents in web mining, segmented pictures in image processing, or time series in speech recognition and finance, have particular structures which contain relevant information for the statistical problem but can hardly be encoded into finite-dimensional vector representations.
Kernel methods are a class of algorithms well suited for such problems. Indeed they extend the applicability of many statistical methods initially designed for vectors to virtually any type of data, without the need for explicit vectorization of the data. The price to pay for this extension to non-vectors is the need to define a so-called positive definite kernel function between the objects, formally equivalent to an implicit vectorization of the data. The "art" of kernel design for various objects have witnessed important advances in recent years, resulting in many state-of-the-art algorithms and successful applications in many domains.
The goal of this course is to present the mathematical foundations of kernel methods, as well as the main approaches that have emerged so far in kernel design. We will start with a presentation of the theory of positive definite kernels and reproducing kernel Hilbert spaces, which will allow us to introduce several kernel methods including kernel principal component analysis and support vector machines. Then we will come back to the problem of defining the kernel. We will present the main results about Mercer kernels and semigroup kernels, as well as a few examples of kernel for strings and graphs, taken from applications in computational biology, text processing and image analysis. Finally we will touch upon topics of active research, such as large-scale kernel methods and deep kernel machines.
|Jan 13||JM+JPV||Kernels, RKHS, examples||1-41||Lecture 1, Lecture 2||Homework 1||Uniqueness of the RKHS Aronszajn's theorem|
|Jan 20||JM||Smoothness functional, Kernel trick, Representer theorem||42-82||Lecture 3, Lecture 4, Lecture 5|
|Jan 27||JPV||Kernel ridge and logistic regression||83-114||Lecture 6||Homework 2 Draft solution|
|Feb 3||JM||Large-margin classifiers, SVMs||115-165||Lecture 7, Lecture 8||Bartlett et al. 2003|
|Feb 10||JPV||Unsupervised learning, kernel PCA, K-means, CCA||166-202||Lecture 9, Lecture 10||Homework 3|
|Feb 24||JM||Green, Mercer, Herglotz, Bochner and friends||203-310||Lecture 11a Lecture 11b Lecture 11d||Lecture 11c|
|Mar 3||JPV||Kernels for graphs, kernels on graphs||436-549||Lecture 12a Lecture 12b||Homework 4|
|Mar 10||JPV||MKL, large-scale learning with kernels||551-621||Lecture 13a Lecture 13|
|Mar 17||JM||Deep kernel machines||630-714||Lecture 14a||Final Homework||Lecture 14b and Lecture 14c|