1
CS 189: Introduction to Machine Learning - Discussion 1
1. Probability Review
There are n archers all shooting at the same target (bullseye) of radius 1. Let the
score for a particular archer be dened to be the distance away from the center (the
lower t

CS 189 Spring 2016
Discussion 2: Support Vector Machines
1. Support Vector Machines
a. We typically frame an SVM problem as trying to maximize the margin. Explain intuitively
why a bigger margin will result in a model that will generalize better, or perfo

Discussion 1
Math Review
1. Probability Review
There are n archers all shooting at the same target (bullseye) of radius 1. Let the score
for a particular archer be defined to be the distance away from the center (the lower
the score the better, and 0 is t

CS 189
Fall 2016
Introduction to
Machine Learning
Midterm
Do not open the exam before you are instructed to do so.
The exam is closed book, closed notes except your one-page cheat sheet.
Usage of electronic devices is forbidden. If we see you using an

Chapter 13
The Multivariate Gaussian
In this chapter we present some basic facts regarding the multivariate Gaussian distribution.
We discuss the two major parameterizations of the multivariate Gaussianthe moment
parameterization and the canonical paramet

CS 189
Spring 2014
Introduction to
Machine Learning
Final
You have 3 hours for the exam.
The exam is closed book, closed notes except your one-page crib sheet.
Please use non-programmable calculators only.
Mark your answers ON THE EXAM ITSELF. If you

CS 189
Spring 2013
Introduction to
Machine Learning
Final
You have 3 hours for the exam.
The exam is closed book, closed notes except your one-page (two sides) or two-page (one side) crib sheet.
Please use non-programmable calculators only.
Mark your

CS 189
Spring 2016
Introduction to
Machine Learning
Final
Please do not open the exam before you are instructed to do so.
The exam is closed book, closed notes except your two-page cheat sheet.
Electronic devices are forbidden on your person, including

Introduction to
Machine Learning
CS 189
Spring 2014
Midterm
You have 2 hours for the exam.
The exam is closed book, closed notes except your one-page crib sheet.
Please use non-programmable calculators only.
Mark your answers ON THE EXAM ITSELF. If yo

CS 189
Spring 2013
Introduction to
Machine Learning
Midterm
You have 1 hour 20 minutes for the exam.
The exam is closed book, closed notes except your one-page crib sheet.
Please use non-programmable calculators only.
Mark your answers ON THE EXAM ITS

CS189/CS289A
Introduction to Machine Learning
Lecture 8: More on the Multivariate Normal Distribution
Peter Bartlett
February 12, 2015
1 / 36
Outline
2 / 36
Outline
Review: Diagonal covariance matrices.
2 / 36
Outline
Review: Diagonal covariance matrices.

1
CS 189: Introduction to Machine Learning - Discussion 6
1. Overfitting, model selection and regularization
Consider the set of training points in the following figure.
There are two classes 0 and 1 and two features (the x and y axes). We wish to build a

1
CS 189: Introduction to Machine Learning - Discussion 5
Let yi R, x, w Rd . For each problem assume we have data points x1 , ., xn .
1. Weighted Least Squares
In our traditional least squares scenario, we minimize the least squares error, or:
L() =
n
X

CS189/CS289A
Introduction to Machine Learning
Lecture 6:
Peter Bartlett
February 5, 2015
1 / 43
Outline
2 / 43
Outline
Recall: Gaussian class conditionals lead to a logistic posterior.
2 / 43
Outline
Recall: Gaussian class conditionals lead to a logistic

CS189/CS289A
Introduction to Machine Learning
Lecture 4: Decision Theory
Peter Bartlett
January 29, 2015
1 / 34
Outline
2 / 34
Outline
Decision theory
2 / 34
Outline
Decision theory
Loss functions
2 / 34
Outline
Decision theory
Loss functions
Probabilisti

CS189/CS289A
Introduction to Machine Learning
Lecture 5:
Peter Bartlett
February 3, 2015
1 / 19
Outline
2 / 19
Outline
Two facts from probability theory
2 / 19
Outline
Two facts from probability theory
Generative and discriminative models:
Gaussian class

CS189/CS289A
Introduction to Machine Learning
Lecture 2: Linear classiers
Peter Bartlett
January 22, 2015
1 / 30
Linear Classiers:
x Rd , y cfw_1, 1
2 / 30
Linear Classiers
1
Training
3 / 30
Linear Classiers
1
Training
Collect labeled data.
3 / 30
Linear

CS 189
Spring 2014
Introduction to
Machine Learning
Final
You have 3 hours for the exam.
The exam is closed book, closed notes except your one-page crib sheet.
Please use non-programmable calculators only.
Mark your answers ON THE EXAM ITSELF. If you

CS 189 Spring 2016
Discussion 4: MLE and Multivariate Gaussian
1. MLE of the Laplace Distribution
Let X have a Laplace distribution with density
1
|x |
p(x; , b) =
exp
2b
b
Suppose that n samples x1 , . . . , xn are drawn independently according to p(x;

1
CS 189: Introduction to Machine Learning - Discussion 5
Let yi R, x, w Rd . For each problem assume we have data points x1 , ., xn .
1. Weighted Least Squares
In our traditional least squares scenario, we minimize the least squares error, or:
L() =
n
X

1
CS 189: Introduction to Machine Learning - Discussion 6
1. Overfitting, model selection and regularization
Consider the set of training points in the following figure.
There are two classes 0 and 1 and two features (the x and y axes). We wish to build a

Introduction to
Machine Learning
CS 189
Spring 2014
Midterm
You have 2 hours for the exam.
The exam is closed book, closed notes except your one-page crib sheet.
Please use non-programmable calculators only.
Mark your answers ON THE EXAM ITSELF. If yo

5/12/2017
Neural networks and deep learning
CHAPTER 1
Using neural nets to recognize handwritten digits
The human visual system is one of the wonders of the world.
Consider the following sequence of handwritten digits:
Most people effortlessly recognize t

Name:
Student ID:
CS 189: Introduction to Machine Learning
Homework 1
Due: September 13, 2016 at 11:59pm
Instructions
This homework includes both a written portion and a coding portion.
We prefer that you typeset your answers using LATEX. Neatly handwri

C OLOR AND I MAGING
11
CS184: C OMPUTER G RAPHICS AND I MAGING
April 11, 2017
1
Cameras and Lenses
1.1 Terminology
When dealing with cameras and lenses, it is easy to get bogged down by the various terms
and mix up how they all relate to one another. Some

Discussion 07:
Path Tracing and
Material Modeling
Computer Graphics and Imaging
UC Berkeley CS184/284A, Spring 2017
Path Traced Global Illumination
What method will you use to render this?
CS184/284A, Discussion 07
Spring 2017
Is Path Tracing Suitable to

CS189: Introduction to Machine Learning
Homework 6
Due: 11:59 pm, Thursday December 1st, 2016
This assignment contains some optional questions. Feel free to work on them. We encourage
you to try them out to expand your understanding. They will not be gra