Quiz 7
1. While the primal and dual formulations of a problem are stated in terms of
different variables, the number of variables in both cases is the same.
A) True; B) False
False, the nr of variables in the du
Prediction and Search in Probabilistic Worlds
Markov Systems, Markov
Decision Processes, and
Dynamic Programming
Note to other teachers and users of
these slides. Andrew would be delighted
if you found this source material useful in
giving your own lectur
CS273 Midterm Exam
Introduction to Machine Learning: Winter 2015
Tuesday February 10th, 2014
Your name:
Your UCINetID (e.g., myname@uci.edu):
Your seat (row and number):
Total time is 80 minutes. READ THE EXAM FIRST and organize your time; dont spend
too
CS273 Final Exam
Introduction to Machine Learning: Winter 2015
Tuesday March 17th, 2015
Your name:
Your UCINetID (e.g., myname@uci.edu):
Your seat (row and number):
Total time is 1 hour 50 minutes. READ THE EXAM FIRST and organize your time; dont
spend t
CS273a Homework #1
Introduction to Machine Learning: Fall 2016
Due: Monday October 3rd, 2016
Write neatly (or type) and show all your work!
This homework (and many subsequent ones) will involve data analysis and reporting on methods and results using Pyth
CS273a Homework #2
Introduction to Machine Learning: Fall 2016
Due: Friday October 14th, 2016
Write neatly (or type) and show all your work!
Problem 1: Linear Regression
For this problem we will explore linear regression, the creation of additional featur
CS273a Midterm Exam
Machine Learning & Data Mining: Fall 2013
Thursday November 7th, 2013
Your name:
Name of the person in front of you (if any):
Name of the person to your right (if any):
Total time is 1:15. READ THE EXAM FIRST and organize your time; d
CS273a Homework #3
Introduction to Machine Learning: Fall 2016
Due: Friday October 28th, 2016
Write neatly (or type) and show all your work!
Please remember to turn in at most two documents, one with any handwritten solutions, and
one PDF le with any elec
+
Machine Learning and Data Mining
Linear regression
Prof. Alexander Ihler
Supervised learning
Notation
Features
x
Targets
y
Predictions
Parameters
Training data
(examples)
Features
Feedback /
Target values
Program (Learner)
Characterized by
some
+
Machine Learning and Data Mining
Bayes Classifiers
Prof. Alexander Ihler
A basic classier
Training data D=cfw_x(i),y(i), Classier f(x ; D)
Discrete feature vector x
f(x ; D) is a con@ngency table
Ex: credit ra@ng predic@on (bad/good)
X1 = inco
+
Machine Learning and Data Mining
Support Vector Machines
Prof. Alexander Ihler
Linear Classifiers
Which decision boundary is better?
Both have zero training error (perfect training accuracy)
But, one of them seems intuitively better
Feature 2, x2
Fea
+
Machine Learning and Data Mining
Linear classification
Prof. Alexander Ihler
Supervised learning
Notation
Features
x
Targets
y
Predictions
Parameters
Learning algorithm
Program (Learner)
Training data
(examples)
Features
Feedback /
Target values
Char
CS273a Final Exam
Introduction to Machine Learning: Fall 2013
Thursday December 12th, 2013
Your name:
Your UCInetID (all caps):
Your Seat (row and number):
Total time is 1:50. READ THE EXAM FIRST and organize your time; dont spend too long
on any one pro
C8273a Midterm Exam
Machine Learning & Data Mining: Fall 2013
Thursday November 7th, 2013
Your name: buUnQ 003
Name of the [person in front of you (if any):
Name of the person to your right (if any):
a Total time is 1:15. READ THE EXAM FIRST and organize
+
Machine Learning and Data Mining
Clustering (1): Basics
Prof. Alexander Ihler
Unsupervised learning
Supervised learning
Predict target value (y) given features (x)
Unsupervised learning
Understand patterns of data (just x)
Useful for many reasons
+
Machine Learning and Data Mining
Support Vector Machines
Prof. Alexander Ihler
Linear Classifiers
Which decision boundary is better?
Both have zero training error (perfect training accuracy)
But, one of them seems intuitively better
Feature 2, x2
Fea
+
Machine Learning and Data Mining
Multi-layer Perceptrons & Neural Networks:
Basics
Prof. Alexander Ihler
Linear Classifiers (Perceptrons)
Linear Classifiers
a linear classifier is a mapping which partitions feature space using a
linear function (a str
+
Machine Learning and Data Mining
VC Dimension
Prof. Alexander Ihler
Slides based on Andrew Moores
Learners and Complexity
Weve seen many versions of underfit/overfit trade-off
Complexity of the learner
Representational Power
Different learners have
+
Machine Learning and Data Mining
Linear classification
Prof. Alexander Ihler
Supervised learning
Notation
Features
x
Targets
y
Predictions
Parameters
Learning algorithm
Program (Learner)
Training data
(examples)
Features
Feedback /
Target values
Char
Machine Learning and Data Mining
Introduction
Prof. Alexander Ihler
CS 178 / CS273a
Winter 2015
Artificial Intelligence (AI)
Building intelligent systems
Lots of parts to intelligent behavior
Darpa GC (Stanley)
RoboCup
Chess (Deep Blue v. Kasparov)
Mach
import numpy as np;
import matplotlib.pyplot as plt;
import mltools as ml;
#1.a. Load the #data/curve80.txt# data set, and split it into 75% / 25%
training/test.
data = np.genfromtxt("data/curve80.txt", delimiter=None)
X = data[:,0]
X = X[:,np.newaxis] #
HW1s
October 6, 2016
In [1]: import numpy as np
np.random.seed(0)
import mltools as ml
import matplotlib.pyplot as plt
%matplotlib inline
1
# use matplotlib for plotting with inline plots
P1: Data Exploration
In [2]: iris = np.genfromtxt("data/iris.txt",d
+
Machine Learning and Data Mining
Introduction
Prof. Alexander Ihler
Artificial Intelligence (AI)
Building intelligent systems
Lots of parts to intelligent behavior
Darpa GC (Stanley)
RoboCup
Chess (Deep Blue v. Kasparov)
Machine learning (ML)
One (imp
data/curve80.txt
data[:,0]
data[:,1]
x
y
X = data[:,0]
X = X[:,np.newaxis]
# code expects shape (M,N) so make sure it's 2-dimensional
Y = data[:,1]
# doesn't matter for Y
Xtr,Xte,Ytr,Yte = ml.splitData(X,Y,0.75) # split data set 75/25
linearRegress
y
x
x
ICS 273A
Intro Machine Learning
Lecture 3
decision trees, random forests,
bagging, boosting.
Decision Trees
Problem: decide whether to wait for a table at a restaurant,
based on the following attributes:
1. Alternate: is there an alternative restaurant ne
Machine Learning and Data Mining
Introduction
Prof. Alexander Ihler
Fall 2012
Artificial Intelligence (AI)
Building intelligent systems
Lots of parts to intelligent behavior
Darpa GC (Stanley)
RoboCup
Chess (Deep Blue v. Kasparov)
Machine learning (ML)
Machine Learning and Data Mining
Nearest neighbor methods
Prof. Alexander Ihler
Fall 2012
Supervised learning
Notation
Features
x
Targets
y
Predictions
Parameters
Learning algorithm
Program (Learner)
Training data
(examples)
Features
Feedback /
Target