10/14/13
Kernels
Definition: A function k(x, z) that can be expressed as a dot
product in some feature space is called a kernel.
More on kernel functions
In other words, k(x, z) is a kernel if there exists
such that
k ( x , z) = ( x ) | ( z)
: X 7! F
1
Ex
3.1 Written Questions
3.1.1 HMM for DNA Sequence [6|] points)
In this problem. you will use Hl'illil to decode a. simple DNA. sequenoe. It is well known that a DNA sequence
is a eerie-s of components from 21.0. G. T. Now lets assume there is one hidden va
6/22/2016
CS545:MachineLearning[assignments:assignment_6]
Assignment 6: Controlling a Dynamic Marble with
ReinforcementLearning
In this assignment, you will be modifying the reinforcement learning code used to solve the
dynamic marble problem. You will be
6/22/2016
CS545:MachineLearning[assignments:assignment_2]
Assignment 2:
Visualization
Mixture
of
Gaussians
and
2D
Part1:ImplementandTestMixtureofGaussians
Implement the Mixture of Gaussians algorithm, or use the implementation presented in class, and
test
6/22/2016
CS545:MachineLearning[assignments:assignment_5]
Assignment 5: Classification Using LDA, QDA, and
LinearandNonlinearLogisticRegression
Implement the above classification procedures. Demonstrate them using toy data that is one
dimensional. Then ap
6/22/2016
CS545:MachineLearning[assignments:assignment_4]
Assignment 4: Nonlinear Regression with Neural
Networks
Part1
Implement the NeuralNetwork class from the lecture notes. Test it with a very simple data set
consisting of one input and one output. P
Implementation and Use of The K-Means Algorithm
Rahul Shanbhog
September 11, 2014
Contents
Abstract
k-means algorithm can be described as an unsupervised implementation of clustering. In the current
assignment, a python working version of the same is pres
10/10/13
Evaluating classifier performance
Evaluating and using ML classifiers:
model selection
The simplest evaluation protocol:
q
Train a classifier on the training set
q
Chapter 2
Divide your labeled data into a training set and test set.
q
Classify th
10/21/13
Measuring distance
How to measure closeness?
Distance based models
Distance measures for continuous data:
The Euclidean distance:
v
ud
uX
Dis (x, y) = |x y| = t (x
Chapter 8
2
2
i
yi ) 2 =
i=1
p
(x
y )| (x
y)
(based on the 2-norm)
1
Distance base
10/21/13
Clustering
Distance based clustering
Clustering is the art of finding groups in data (Kaufman and
Rousseeuw, 1990).
What is a cluster?
Group
Chapter 8
of objects separated from other clusters
16
14
16
12
14
12
10
10
8
8
6
6
4
4
2
0
2
4
6
8
10
12
Kernel methods for predicting
protein-protein interactions
Asa Ben-Hur
Department of Computer Science
Colorado State University
Kernel methods for predicting protein-protein interactions p.1/33
Background: from DNA to Protein
Kernel methods for predicting
10/29/13
Probability theory
A crash course in probability and Nave
Bayes classification
Random variable: a variable whose possible values are numerical
outcomes of a random phenomenon.
Examples: A persons height, the outcome of a coin toss
Chapter 9
Disti
10/7/13
Handling more than two classes
v
Beyond Binary Classification
v
Chapter 3
v
v
Some classifiers methods can only be used for binary
classification
Can we somehow use a binary classifier to do multi-class
classification?
How to evaluate multi-class
10/7/13
Kernels
Definition: A function k(x, z) that can be expressed as a dot
product in some feature space is called a kernel.
Kernel-based learning algorithms
In other words, there exists
such that
Chapter 7
: X 7! F
k ( x , z) = ( x ) | ( z)
1
2
Kernel
9/4/13
Using classifiers
At this point we have learned two classification algorithms:
Evaluating and using ML classifiers
q
The closest centroid classifier
q
The perceptron algorithm
+
+
Chapter 2
+
+
p
How do we measure how well they
+
w=pn
(p+n)/2
+
+
+
9/3/13
Preliminaries
Linear models: the perceptron and
closest centroid algorithms
Definition: The Euclidean dot product between two vectors is
d
the expression
X
wT x =
w i xi
i=1
The dot product is also referred to as inner product or scalar
product.
Ch