Recitation Assignment #1 Please do the following problems during your recitation session, including any additional problems given to you by your TA. Within 72hrs of your recitation session, you must upload the complete solutions to these problems to Sakai, so that your TAs can evaluate them in a timely fashion. For this recitation, please show […]
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Homework 8 There are 5 time slots that you can utilize to transmit the data. You may transmit the data quickly using just one time slot or you may transmit the data slower using all 5 time slots or any number of time slots between 1 and 5. If you can get half or more
This document describes one of the available topics for the MSc-project in Financial Mathematics. The focus is on optimization of a static portfolio of derivative instruments on given underlying assets. A primary goal is to set up and solve portfolio optimization problems that account for non-Gaussian return distributions. Such optimization problems can rarely be solved
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Represent the audio signal as a sequence of features, e.g., Mel-frequency cepstral coefficients (MFCCs). (I) Develop and evaluate a conventional activity recognition system based on using the Gaussian Mixture Model (GMM) – perform the training of the model for each activity with corresponding data. (II) Develop and evaluate a GMM-UBM system –
for those functions. Solve the following tasks, and you should keep in mind that shorter codes have a positive impact on your mark. Tasks: You are going to work with datasets dataOLS.txt and IndustriesUS.txt. The first dataset contains 4 variables: sorted as date, Market excess returns, SMB factor, HML factor and risk-free asset returns. Dataset
Homework Assignment #5 All data sets relevant for this assignment can be found at the UCI Machine Learning Repository, at http://archive.ics.uci.edu/ml/ Problem 1 (45 points) Download three different data sets (of your choice) for binary classification. Plan on taking only the data with real-valued features; however, if some features are categorical, say
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The assignment will be updated on a regular basis. Clarifications and modifications will be added as and when required. Note that answers should be submitted electronically, using subversion. You will be required to commit one or more files for each question, with specific file names. If you want to commit additional files, for functions that
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Programming Assignment Due Wednesday, May 6th Sparse (Linear) Logistic Regression Suppose that we have a collection of vectors xi 2 Rn, i = 1, 2, …, N, and that for each i there is a value yi 2 {0, 1}. Given a new value x 2 Rn, we want to predict the value y to
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Differential Equations. Computer Practical 1 (Week 2. Explicit and Implicit Euler methods for finding numerical solutions to the initial value problems; first-order ODEs) February 6, 2014 Aim: To learn how to solve numerically elementary initial value problems using Explicit and Implicit Euler method Outcomes: written report (must be uploaded to the Blackboard) Due date: Friday,
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4071 Project Empirical Study of Moving Average Crossover Strategies using Different Measures Empirical Study of Different Measures of Moving Average Strategies Introduction Technical analysis has been around for quite a long time and as the years passed. We can find hundreds of indicators in different forms, while some indicators are more popular than