homework 3
Weifeng She
10/12/2016
1. Write down detailed formulas for the gradient of the loss function in the case of logistic
regression, and write detailed pseudo code for training a LR model based
project2.R
Weifeng She
11/21/2016
1. Use linear regression to predict profit based on all available numeric variables. Graph the train and test
MSE as a function of the train set size (averaged over 1
hw2
Weifeng She
August 29, 2016
1.Using the mpg data, describe the relationship between highway mpg and car manufacturer. Describe which
companies produce the most and least fuel e!cient cars, and dis
Problem 1.
Problem Statement
Write down detailed formulas for the gradient of the loss function in the case of logistic
regression, and write detailed pseudo code for training a LR model based on grad
homework 3
Weifeng She
10/12/2016
Note: question 1 is writen in latex and is attached at the last of the document. All other question is compiled
by R markdown.
2. Implement in R logistic regression b
2/13/2014
Ridge Regression, LASSO and Elastic Net
Ridge Regression, LASSO and Elastic Net
A talk given at NYC open data meetup, find more at www.nycopendata.com
Yunting Sun
Google Inc
file:/Users/ytsu
Machine learning methodology: Overfitting,
regularization, and all that
CS194-10 Fall 2011
CS194-10 Fall 2011
1
Outline
Measuring learning performance
Overfitting
Regularization
Cross-validation
Linear regression, Logistic regression,
and Generalized Linear Models
David M. Blei
Columbia University
November 18, 2014
1
Linear Regression
Linear regression helps solve the problem of predicting a
Project: 1
Name: Ajay Joshi
1. 798 rows has been removed.
2. Figure:2.1 Year Vs Runtime
The figure 2.1 shows that the runtime values are the lowest from 1888 till around 1912
and there's gradual incre
#Name: Ajay Joshi
#-#question 1: Using the mpg data, describe the relationship between highway mpg and
#car manufacturer. Describe which companies produce the most and least
#fuel efficient cars, and
#HomeWork1
#Name: Ajay Joshi
#Course: CSE 6242
#Git:903270434
#=
#2.Implement a function that computes the log of a factorial value of an integer using a for
loop.
LoopLogFactorial <- function(a) cfw_
The file movies_merged1 contains a dataframe with the same name that has 40K rows and 39 columns. Each row represents a movie title and each column represents a descriptor such as Title, Actors, and B
1.Use linear regression to predict profit based on all available numeric variables. Graph the train and test MSE as a function of the train set size (averaged over 10 random data partitions as describ
Transformations and Polynomial Regression
One of the first steps in the construction of a regression model is to hypothesize
the form of the regression function. We can dramatically expand the scope o
Computational Photography
Assignment #2: Image I/O & Libraries
Shiyan Jiang
Spring 2017
CS 6475 - Spring 2017
numberOfPixels
Discuss your approach and code for finding the number of pixels in a graysc
Computational Photography
Assignment #1: A Photograph is a Photograph
Shiyan Jiang
Spring 2017
CS 6475 - Spring 2017
Soup Dumpling
CS 6475 - Spring 2017
2
Details of the Picture
Image Caption
Food is
Computational Photography
Assignment #7: Feature Detection & Matching
Shiyan Jiang
Spring 2017
CS 6475 - Spring 2017
General Template Notes
The following notes apply to the entire write-up.
Note 1: Pa
Computational Photography
Assignment #5
Camera Obscura
Shiyan Jiang
Spring 2017
CS 6475
General Template Notes
(Delete this slide after reading)
The following notes apply to the entire write-up.
Note
Computational Photography
Assignment #6: Blending
Shiyan Jiang
Spring 2017
CS 6475 - Fall 2016
General Template Notes
(Delete this slide after reading)
The following notes apply to the entire write-up
Computational Photography
Assignment #8
Panoramas
Shiyan Jiang
Spring 2017
CS 6475
General Template Notes
The following notes apply to the entire write-up.
Note 1: Parts require you to use images you
Computational Photography
Assignment #4: Gradients & Edges
Shiyan Jiang
Spring 2017
CS 6475 - Spring 2017
Part 1: normalizeImage
What is the purpose of normalizeImage()? Describe this function in your
Computational Photography
Assignment #9
HDR
Shiyan Jiang
Spring 2017
CS 6475
General Template Notes
The following notes apply to the entire write-up.
Note 1: Parts require you to use images you have t
Computational Photography
Assignment #3: Epsilon Photography
Shiyan Jiang
Spring 2017
CS 6475 - Spring 2017
A Brief Look at My Epsilon Project
Image 1
Image 2
Image 3
Image 4
Image 5
These photos of f
Computational Photography
Assignment #10
Photos of Space
Shiyan Jiang
Spring 2017
Computational Photography @ GT
Important Note Before Moving On
Assignment 10 does not have a coding portion. Your enti
Project 1: Explore and Prepare Data
CSE6242 - Data and Visual Analy?cs - Spring 2017 Due: Sunday, March
5, 2017 at 11:59 PM UTC-12:00 on T-Square
Note: This project involves get
Project 2: Modeling and Evaluation
Data
We will use the same dataset as Project 1: movies_merged.
Objective
Your goal in this project is to build a linear regression mo
GTID: sjiang98
0. Data Preprocessing
4 sample images, one from each class, along with their class labels to demonstrate youve
read the data correctly.
train_0_1 (class
lable: 0)
train_3_5 (class
lable
Question 1
i) An approach to utilize C logistic regressions is to use the one against all approach.
It is easy to decompose the problem into C classification problem
# Activity: Time Series Analysis
library(zoo)
library(xts)
# basic time series package
# eXtensible Time Series package
data_dir <- "data"
label_dir <- "labeled_windows"
df <read.csv("~/Desktop/CSE624