Machine Learning
Math Essentials
Jeff Howbert
Introduction to Machine Learning
Winter 2012
1
Areas of math essential to machine learning
z
z
z
Machine learning is part of both statistics and computer
science
Probability
Statistical inference
Validation

CS 63
Machine Learning
Chapter 18.1-18.3, 19.1,
skim 20.4-20.5
Adapted from slides by
Tim Finin and
Marie desJardins.
Some material adopted
from notes by Chuck Dyer
Outline
Machine learning
What is ML?
Inductive learning
Supervised
Unsupervised
Deci

The Perceptron and its Learning Rule
Carlo U. Nicola, SGI FH Aargau
With extracts from publications of :
M. Minsky, MIT, Demuth, U. of Colorado,
D.J. C. MacKay, Cambridge University
Perceptron
Bias b is sometimes called
(i) Single layer ANN
(ii) It works

Neural Networks
Some slides adapted from Tom Mitchell
Neural Networks
Quick review of neural net basics
Primitive units (perceptrons, linear and sigmoid)
Perceptron learning
Backpropagation learning
Issues:
Representation
capabilities
Hypothesis space

CHAPTER
ARTIFICIAL
NEURAL
NETWORKS
Artificial neural networks (ANNs) provide a general, practical method for learning
real-valued, discrete-valued, and vector-valued functions from examples. Algorithms
such as BACKPROPAGATION
use gradient descent to tune

Machine Learning
A Probabilistic Perspective
Kevin P. Murphy
The MIT Press
Cambridge, Massachusetts
London, England
1
1.1
Introduction
Machine learning: what and why?
We are drowning in information and starving for knowledge. John Naisbitt.
We are enterin

CS 63
Machine Learning
Chapter 18.1-18.3, 19.1,
skim 20.4-20.5
Adapted from slides by
Tim Finin and
Marie desJardins.
Some material adopted
from notes by Chuck Dyer
Outline
Machine learning
What is ML?
Inductive learning
Supervised
Unsupervised
Deci

Biological Inspiration
Artificial Neural Network (ANN)
loosely based on biological neuron
Each unit is simple, but many
connected in a complex network
If enough inputs are received
Neuron gets excited
Passes on a signal, or fires
ANN different to bi

Artificial Neural Network
Lecture Module 22
Neural Networks
Artificial neural network (ANN) is a machine learning
approach that models human brain and consists of a
number of artificial neurons.
Neuron in ANNs tend to have fewer connections than
biologic

UNIVERSITY EXAMINATIONS UNIVERSITEITSEKSAMENS
U N I SA m
3M1 afnra
COS4852 January/February 2013
MACHINE LEARNING
Duration 3 Hours 75 Marks
EXAMINATION PANEL AS APPOINTED BY THE DEPARTMENT
Use of a nonâ€”programmabie pocket calculator Is permlsmble
Ciosed

Chapter 2
Fundamentals of Machine Learning
2.1 Learning Methods
Learning is a fundamental capability of neural networks. Learning rules are algorithms for finding suitable weights W and/or other network parameters. Learning of
a neural network can be view

Lecture 13 Perceptrons
Machine Learning
March 16, 2010
Last Time
Hidden Markov Models
Sequential modeling represented in a Graphical
Model
2
Today
Perceptrons
Leading to
Neural Networks
aka Multilayer Perceptron Networks
But more accurately: Multil

Announcements
Homework 5 due today, October 30
Book Review due today, October 30
Lab 3 due Thursday, November 1
Homework 6 due Tuesday, November 6
Current Event
Kay - today
Chelsea - Thursday, November 1
CS 484 Artificial
Intelligence
1
Neural Networks

Library and Information Research
Volume 33 Number 105 2009
_
Interviews via VoIP: Benefits and Disadvantages within a PhD
study of SMEs
Naomi V. Hay-Gibson
Abstract
The benefits and disadvantages of Voice over Internet Protocol (VoIP) are
explored as part

Learning from Example
Given some data build a model to make
predictions
Linear Models (Perceptrons).
Support Vector Machines.
House Price for a given size
Many relationships
We know are linear
V=IR (ohms law)
F=ma (Newton's 2nd)
Pv=nRT (gas law)
Can yo

Machine Learning
Lecture 2
Perceptron
G53MLE | Machine Learning | Dr Guoping Qiu
1
Perceptron - Basic
Perceptron is a type of artificial neural
network (ANN)
G53MLE | Machine Learning | Dr
Guoping Qiu
2
Perceptron - Operation
It takes a vector of real-va

Announcements
Homework 5 due today, October 30
Book Review due today, October 30
Lab 3 due Thursday, November 1
Homework 6 due Tuesday, November 6
Current Event
Kay - today
Chelsea - Thursday, November 1
CS 484 Artificial
Intelligence
1
Neural Networks

Artificial Neural Networks for Beginners
Carlos Gershenson
C.Gershenson@sussex.ac.uk
1. Introduction
The scope of this teaching package is to make a brief induction to Artificial Neural
Networks (ANNs) for people who have no previous knowledge of them. We