COMP9417 – Machine Learning Homework 1: Regularized Regression & Numerical Optimization Introduction In this homework we will explore some algorithms for gradient based optimization. These algorithms have been crucial to the development of machine learning in the last few decades. The most famous example is the backpropagation algorithm used in deep learning, which is in […]
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COMP9417 – Machine Learning Homework 2 Introduction In this homework we first take a closer look at feature maps induced by kernels. We then ex- plore a creative use of the gradient descent method introduced in homework 1. We will show that gradient descent techniques can be used to construct combinations of models from a
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NAME OF CANDIDATE: …………………………………………….. STUDENT ID: …………………………………………….. SIGNATURE: …………………………………………….. THE UNIVERSITY OF NEW SOUTH WALES Term 2, 2022 COMP9417 Machine Learning and Data Mining – Final Examination 1. TIME ALLOWED — 24 HOURS 2. THIS EXAMINATION PAPER HAS 12 PAGES 3. TOTAL NUMBER OF QUESTIONS — 4 4. ANSWER ALL 4 QUESTIONS 5. TOTAL MARKS
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Unit Guide Data visualisation and analytics Summer semester B, 2020 We acknowledge and pay respects to the Traditional Owners and Elders – past, present and emerging – of the lands and waters on which Monash University operates. Handbook link: http://monash.edu.au/pubs/2020handbooks/units/ETX2250.html The information contained in this unit guide is correct at time of publication. The University
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COMP9727 Recommender Systems Assignment 1 – 22T2 Due: 1st July, 17:00 AEST Total Mark: 50 Introduction In this assignment, you will be required to manually implement a few recommen- dation algorithms in Python as well as answer some corresponding questions individually. Other than this spec, all the required files can be found in ‘a1.zip’, which
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COMP4336/9336 Mobile Data Networking 2022 Term 2 Individual Term Project: Due 5pm Friday 29 July 2022 (Week 9) Assessment Weighting: 25% Project Specifications and Marking Criteria (5 pages): Released 16 June 2022 This is the complete specification of the term project. You are encouraged to discuss the project or any questions in the Project Forum
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Unreal Estate Final Report Clive Chen Emmanuel Kozman Gagandeep Nain Table of Contents Introduction 2 Context 2 Project Purpose 7 Project Design 8 Overview of Components 8 Diagrams 11 UML (use link for full image) 11 ER Diagram 12 Software Architecture Diagram 13 User Scenarios 14 All Users 14 Renter 15 Advertiser 16 Sequence Diagram
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ECON425: Machine Learning Winter 2022 Question 1. Typical machine learning algorithms can be used to address both supervised or unsupervised problems. The predictions of these algorithms are either continuous values or discrete labels. Therefore, there are four types of machine learning algorithms, as summarized in the following 1. Supervised, continuous 2. Supervised, discrete 3. Unsupervised,
ECON425: Machine Learning Winter 2022 Question 1. Typical machine learning algorithms can be used to address both supervised or unsupervised problems. The predictions of these algorithms are either continuous values or discrete labels. Therefore, there are four types of machine learning algorithms, as summarized in the following 1. Supervised, continuous 2. Supervised, discrete 3. Unsupervised,
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Monash University FIT5202 – Data processing for Big Data Assignment 2B: Real-time stream processing on big data Due Date : Sunday, 17th October 2021 11:00 pm Weight : 10% percent of the total mark Background Required datasets (available in Moodle): – A compressed file flight-delays.zip. – This zip file consists of 21 csv files and
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