1. Introduction and JUnit Module 1 Part 1 of 2 Introduction and Agents 1 1 Class Learning Objectives Understand objectives of AI Apply agents 2 The word “AI” has become ubiquitous. But what, exactly, does it mean? This part answers that question. A good portion of AI approaches problems from the perspective of agents—autonomous objects […]
Lecture 2: Working with Data in R – STAT GU4206/GR5206 Statistical Computing & Introduction to Data Science Lecture 2: Working with Data in R STAT GU4206/GR5206 Statistical Computing & Introduction to Data Science Gabriel Young Columbia University September 17, 2021 Gabriel Young Lecture 2: Data in R September 17, 2021 1 / 82 Last Time
FINC3017 Report 1 2021s2 FINC3017 Investments and Portfolio Management Report 1: Asset Allocation and Portfolio Construction Due: 4:00pm, Thursday 16th September 2021 (week 6) Weight: 30% You are hired as an investment consultant with the task of identifying optimal portfolios that meet various criteria for prospective clients and explaining aspects of the portfolio construction process.
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MAST30027: Modern Applied Statistics Assignment 3, 2021. Due: 5pm Wednesday October 6th • This assignment is worth 8% of your total mark. • To get full marks, show your working including 1) R commands and outputs you use, 2) mathematics derivation, and 3) rigorous explanation why you reach conclusions or answers. If you just provide
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Microsoft Word – assignment.docx Objective The key objectives of this assignment are to learn how to train and evaluate a non-trivial machine learning model. More specifically, the task is called “learning-to-rank”. You will be given a set of training features that contain the relevance labels 0 (not relevant), 1 (partially relevant) and 2 (relevant), for
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Microsoft Word – Assignment 2.docx Assignment 2 APACHE-SPARK Due by Wednesday, Oct 13, 2021 1. Note: This assignment needs to be done by using Pyspark or SQL Spark. Submit a compressed archive (zip, tar, etc.) of your code, along with the input and output files and output screenshots (output/input commands with results). Please include a
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Final Assessment Brief: Spike Detection Final Assessment Brief: Spike Detection Modelling in Finance S2 2021 – Updated 25/09/2021 Dinh Tang Background The algorithm in front of you is used to detect spikes from time-series data. This algorithm uses moving averages and moving standard deviations to identify spikes. A spike is defined when a data point,
Smoothing with Levels Australian National University (James Taylor) 1 / 14 J I Exponential Smoothing Overview: Construct forecasts as a weighted average of past observations – smoothing the observed time series Heavier weight is given to more recent observations Weights decrease exponentially with time (thus exponential smoothing) (James Taylor) 2 / 14 Exponential Smoothing Pro
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2 Introduction You have been asked to help the School of Informatics investigate an idea for a service which should allow the students in the School to make the most of their lunch hour where they take a much-needed break from lectures, studying, labs, and practicals like this one! The School of Informatics is considering
F21_APS1070_Project_2 Project 2, APS1070 Fall 2021¶ Anomaly Detection Algorithm using Gaussian Mixture Model [13 Marks] Deadline: OCT 22, 9 PM Academic Integrity This project is individual – it is to be completed on your own. If you have questions, please post your query in the APS1070 Piazza Q&A forums (the answer might be useful to
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