Introduction to Machine Learning

These lecture notes are used in an introductory course in Machine Learning at Purdue University. Strong background in Probability theory, Linear Algebra and Programming are a must.

**Tag(s):**
Machine Learning
Statistics

**Publication date**: 01 Oct 2010

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
234 pages

**Views**: 2,538

Introduction to Machine Learning

These lecture notes are used in an introductory course in Machine Learning at Purdue University. Strong background in Probability theory, Linear Algebra and Programming are a must.

From the Course Description:

Prerequisites:

Strong background in Probability theory, Linear Algebra and Programming are a must.

Alexander J. Smola wrote:With 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.

Prerequisites:

Strong background in Probability theory, Linear Algebra and Programming are a must.

Tweet

About The Author(s)

SVN Vishwanathan is a Professor of Computer Science at the University of California, Santa Cruz. He received his Ph.D in machine learning from the Department of Computer Science and Automation, Indian Institute of Science in 2003. His research goal is to design, analyze, and implement novel machine learning algorithms that take advantage of modern hardware to enable learning on and mining of massive datasets.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors