CSCC11 Introduction to Machine Learning, Winter 2021 Assignment 4, Due Thursday, April 8, 10am This assignment makes use of material from week 8 to week 11 (specifically Chapter 12, Chapter 14 and Chapter 16). To begin the programming component, download a4.tgz from the course website and untar it. A directory A4 will be created; please […]
Supervised versus Unsupervised Learning Sarat C. Dass Department of Mathematical and ComInptruotdeurcStcioienntcoesMHaecrhioint-eWLaetatrnUingiversity Malaysia Campus 79/102 Supervised vs. Unsupervised Learning Machine learning problems can generally be categorized as supervised or unsupervised. The regression and classification problems that we have discussed so far are examples of supervised learning. What does it mean to be supervised? For each
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lecture/12-em-annotated.pdf lecture/13-poe-annotated.pdf lecture/14-xai-annotated.pdf lecture/lecture1-annotated.pdf lecture/lecture10.pdf lecture/lecture11.pdf 1/24 Outline � Latent Variable Models � The Expectation Maximization Procedure � Gaussian Mixture Models � K-Means Clustering � Kernel K-Means 2/24 Motivation PCA of Iris dataset PCA of Boston dataset PCA of Diabetes dataset PCA of Digits dataset Complex data cannot be modeled accurately by standard probability distributions
Outline � Latent Variable Models � The Expectation Maximization Procedure � Gaussian Mixture Models � K-Means Clustering � Kernel K-Means 1/24 Motivation PCA of Iris dataset PCA of Boston dataset PCA of Diabetes dataset PCA of Digits dataset Complex data cannot be modeled accurately by standard probability distributions (e.g. Gaussian) and require a more complex
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JSS Journal of Statistical Software July 2008, Volume 27, Issue 2. http://www.jstatsoft.org/ Panel Data Econometrics in R: The plm Package Yves Croissant Giovanni Millo Universit ́e Lumi`ere Lyon 2 University of Trieste and Generali SpA Abstract Panel data econometrics is obviously one of the main fields in the profession, but most of the models used
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LECTURE 5 TERM 2: MSIN0097 Predictive Analytics A P MOORE MSIN0097 Individual coursework MSIN0097 Individual Coursework assignment has been extended by one week to Friday 5th March 2021 at 10:00 am USING OTHER PEOPLE’S CODE pic.twitter.com/4q4IbLgEB8 — Wojciech Zaremba (@woj_zaremba) February 4, 2021 MACHINE LEARNING JARGON — Model — Interpolating / Extrapolating — Data Bias
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Life Insurance Capital Adequacy Test 255 Albert Street Ottawa, Canada K1A 0H2 www.osfi-bsif.gc.ca Guideline Subject: Life Insurance Capital Adequacy Test No: A Issue Date: September 2016 Effective Date: January 1, 2018 Subsection 515(1), 992(1) and 608(1) of the Insurance Companies Act (ICA) requires federally regulated life insurance companies and societies, holding companies and companies operating
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Lecture 1: Measuring Market Power and Collusion Yiyi Zhou Department of Economics Stony Brook University ECO 637: Empirical IO Overview 1 Measuring Market Power Genesove and Mullin (1998) Bounds on Market Power 2 Collusion and Price Wars Porter (1983) Ellison (1994) Bresnahan (1987) Estimating cost functions without using cost data During the 1960s and 1970s,
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Lecture 1: Introduction to Forecasting UCSD, January 9 2017 Allan Timmermann1 1UC San Diego Timmermann (UCSD) Forecasting Winter, 2017 1 / 64 1 Course objectives 2 Challenges facing forecasters 3 Forecast Objectives: the Loss Function 4 Common Assumptions on Loss 5 Specific Types of Loss Functions 6 Multivariate loss 7 Does the loss function matter?
Center Based Clustering: A Foundational Perspective Pranjal Awasthi and Maria-Florina Balcan Princeton University and Carnegie Mellon University November 10, 2014 Abstract In the first part of this chapter we detail center based clustering methods, namely methods based on finding a “best” set of center points and then assigning data points to their nearest center. In