Introduction to Machine Learning

by Alexander J. Smola, S.V.N. Vishwanathan

S V N Vishwanathan
Alexander J. Smola the availability of cheap storage devices our ability to collect and store large amounts of data is increasing exponentially. Machine learning is a branch of applied statistics which aims to bring to bear tools from statistics in the analysis of such large datasets. This course is a biased journey through some of dominant concepts in machine learning. This is an INTRODUCTORY course in Machine Learning. As such, it will cover basic concepts from both computer science as well as statistics. In first part of the course we will review linear algebra, probability theory, and programming at a very brisk pace. In the next 3 - 4 weeks we will work on some basic machine learning algorithms such as k-means, k-nearest neighbors, Perceptron etc. Finally, we will switch gears and cover a number of more advanced topics. Students will have a chance to implement and test a machine learning algorithm of their choice as part of a medium-scale programming project.

Unknown Binding

First published January 1, 2008

Reviews

[Craig Nicol · Rating 3 out of 5]

High Level overview that then dives deep into equations with little context. Might be a useful companion book to a course, but limited value as a standalone guide.

[Tristan Williams · Rating 3 out of 5]

I found this while looking for intro textbooks in machine learning. The first chapter seemed right, so I went ahead and got it. However, after that, the book REALLY ramps up in difficulty. It was fine for me, but that's only because I have an master's in OR that was mostly convex optimization. Kind of an outlier. If you're looking for an actual "intro" to the field, find something with "practical" in the name and read that instead. If you want theory and are quite familiar with upper-level math (read: Hilbert spaces, Lipschitz continuity), leaf through this one.