Homework #2
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 10/15/15, 8am
Please abide by the Academic Integrity Policy
1
Naive Bayes
The binary Naive Bayes classier has interesting connections to the logistic regression classier. You will
Boosting
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
November 10, 2015
1 / 28
Outline
1
Administration
2
Review of last lecture
3
Boosting
Professor Ameet Talwalkar
CS260 Machine Le
Homework #5
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 11/12/15, 8am
Please abide by the Academic Integrity Policy
1
Bias-Variance Tradeo
Consider a dataset with n data points (xi , yi ), xi Rp1 , drawn from the following linear model:
Homework #3
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 10/29/15, 8am
Please abide by the Academic Integrity Policy
Gradient Descent and Newtons Method
In this problem, you will implement (unregularized/regularized) logistic regression
Homework #6
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 12/4/15, noon
Please abide by the Academic Integrity Policy
1
Clustering
Given a set of data points cfw_xn N , the k-means clustering minimizes the following distortion measure (or
Homework #1
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 10/6/15, 8am
1
Sequence of Coin Flips
Suppose you have a biased coin with probability of heads equal to p. Imagine that you ip this coin until
observing the rst heads. Let X denote
Homework #4
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 10/29/15, 8am
Please abide by the Academic Integrity Policy
1
Linear Regression with Heterogenous Noise
In the standard linear regression model, we consider the model that the obse
PCA
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
December 1, 2015
1 / 21
Outline
1
Administration
2
Review of last lecture
3
Course Evaluation
4
PCA
Professor Ameet Talwalkar
CS260 M
9.1 Overview
9 Deep Learning
Alexander Smola
Introduction to Machine Learning 10-701
http:/alex.smola.org/teaching/10-701-15
A brief history of computers
1970s
1980s
1990s
2000s
2010s
Data
102
103
105
108
1011
RAM
?
1MB
100MB
10GB
1TB
CPU
?
10MF
1GF
100GF
Gaussian and Linear Discriminant Analysis; Multiclass
Classication
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
October 13, 2015
1 / 40
Outline
1
Administration
2
Review of last lect
Course Overview
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
September 23, 2015
1 / 19
Outline
1
Overview of machine learning
What is machine learning?
2
About this Course
3
Review o
Math Review
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
September 23, 2015
1 / 17
Outline
1
Overview
2
Review on Probability
3
Review on Statistics
4
An integrative example
Professo
Neural Networks and Deep Learning
Professor Ameet Talwalkar
November 12, 2015
Professor Ameet Talwalkar
Neural Networks and Deep Learning
November 12, 2015
1 / 16
Outline
1
Review of last lecture
AdaBoost
Boosting as learning nonlinear basis
2
Neural netw
Support Vector Machines
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
November 3, 2015
1 / 34
Outline
1
Administration
2
Review of last lecture
3
Support vector machines Geometric int
Support Vector Machines, Kernel SVM
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
November 5, 2015
1 / 39
Outline
1
Administration
2
Review of last lecture
3
SVM Hinge loss (primal fo
Overtting, Bias / Variance Analysis
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
October 27, 2015
1 / 39
Outline
1
Administration
2
Review of last lecture
3
Basic ideas to overcome o
Linear Regression (continued)
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
October 20, 2015
1 / 37
Outline
1
Administration
2
Review of last lecture
3
Linear regression
4
Nonlinear b
Perceptron and Linear Regresssion
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
October 15, 2015
1 / 39
Outline
1
Administration
2
Review of last lecture
3
Perceptron
4
Linear regress
EM Algorithm
Professor Ameet Talwalkar
Slide Credit: Professor Fei Sha
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
November 24, 2015
1 / 32
Outline
1
Administration
2
Review of last lecture
3
GMMs and Incomplete Data
4
EM Algorithm
Profess
EE219 Project 3
Collaborative Filtering
Winter 2017
Introduction: Recommendation systems and "suggestions" have become an essential part of a lot
of web applications. Examples of these applications include recommended items in online stores, pages
or peop
NAME:
SID#:
Section:
CS 194-10
Introduction to Machine Learning
Fall 2011
Stuart Russell
1
Midterm
You have 80 minutes. The exam is open-book (class-designated reading materials only), open-notes. 80 points total.
Panic not.
Mark your answers ON THE EXAM
Homework #5
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 11/12/15, 8am
Please abide by the Academic Integrity Policy
1
Bias-Variance Tradeoff
Consider a dataset with n data points (xi , yi ), xi Rp1 , drawn from the following linear mode
Homework #3
CS 260: Machine Learning Algorithms
Prof. Ameet Talwalkar
Due: 10/29/15, 8am
Please abide by the Academic Integrity Policy
Gradient Descent and Newtons Method
In this problem, you will implement (unregularized/regularized) logistic regression
Support Vector Machines, Kernel SVM
Professor Ameet Talwalkar
Professor Ameet Talwalkar
CS260 Machine Learning Algorithms
February 27, 2017
1 / 40
Outline
1
Administration
2
Review of last lecture
3
SVM Hinge loss (primal formulation)
4
Kernel SVM
Profess
Neural'Networks'
These slides were assembled by Eric Eaton, with grateful acknowledgement of the many others who made their
course materials freely available online. Feel free to reuse or adapt these slides for your own academic purposes,
provided that yo