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COMP9417 - Machine Learning Homework 2: Bias, Variance and an application of Gradient Descent

COMP9417Machine LearningPythonBiasVarianceGradient Descent

In this homework we revisit the notion of bias and variance as metrics for characterizing the behaviour of an estimator. We then take a look at a new gradient descent based algorithm for combining different machine learning models into a single, more complex, model.

Machine Learning Fundamentals Group Assessment: Model comparison

Machine LearningRMSEFeature EngineeringKNNRegression

Background Information Kevin is a professional real-estate manager. In the past, he relied on using a few important features for home valuation. His boss recently asked him to take the initiative to learn to use big data and machine learning algorithms to value home prices in order to better communicate with customers.

G6061 Fundamentals of Machine Learning Assignment: Photo Classification

G6061Fundamentals of Machine LearningPhoto ClassificationImage ClassificationPythonCNN

The data come from photos, and your task is to come up with a machine learning method for classifying the photos according to whether their content is happy or sad. The data you are given for each photo consists of 3456 features. 3072 of these were extracted from a deep Convolutional Neural Network (CNN) [1], and the remaining 384 are gist features [2]. (You are given all these features as a 1-dimensional array, so you will not be performing any feature extraction on raw images.)

BUSANA7003 Business Analytics Project Final Project: AQR Asset Management

BUSANA7003Business AnalyticsAQR Asset ManagementPythonMachine Learning

You are starting a new job as a Business Analyst at AQR Asset Management, a global investment management firm focused on quantitative investment strategies. Your first task is to analyse the performance of US-listed securities during the COVID-19 market crash.

Project 3: Explore 1 of the given datasets: Breast Cancer, Global IQ Data and Natural Disasters

Data AnalysisMachine LearningR

In Week 9, briefings will be given by your tutors in your Lab class, and then a Video Briefing by your lecturer is in the first ten minutes of the Week 10 Lecture. See a guide to writing a nice report here Download here .

KIT317 Internet of Things and Artificial Intelligence - Assignment 3: Weather Prediction

KIT317KIT717Internet of Things and Artificial IntelligencePythonPHP

IoT devices collect data about the real world to help us make better decisions with that data. Raw data isn’t particularly helpful, so we analyse data to try it into more meaningful information. Sometimes we want to use this data not just to tell us about the past, but also to make inferences about the future.

Machine Learning in Practice Assignment: ML Solutions for Misinformation Detection in Social Media

Machine Learning in PracticePythonExplorationPreparationFeature GenerationClassification

Social media, particularly X (formally known as Twitter), has revolutionized the way information spreads, but it's also an incubator for fake news and misinformation. Misinformation on platform X can evolve from diverse forms and may stem from various sources, whether intentional or not, taking advantage of the platform's viral nature to widen its dissemination

Data 474: Final Project

Data ScienceMachine LearningStatistical LearningVisualizationRegressionClassification

Each team will identify a real data set for which there are interesting questions to answer by finding hidden pattern in data. Then different statistical learning approaches are applied to find the best way to answer these questions. Each team typically consists of 3 students. a team format is strongly encouraged.

Assignment 3: LDA Topic Modeling

LDATopic ModelingPython

LDA is a popular topic modeling algorithm widely used in the fields of Digital Humanities and Social Sciences. In the field of political communication, topic modeling is often applied for analyzing politicians Twitter/X posts, identitying thematic patterns or topics revolving around their posts

ACS341 Machine Learning Coursework Assignment: Household energy consumption

ACS341Machine LearningHousehold energy consumptionPythonPipeline

Accurately predicting household energy consumption allows local power distribution companies to better forecast energy trends and perform demand management1. Power system demand management has gained heightened importance as the world transitions towards renewable energy2

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