CSC 311: Introduction to Machine Learning Lecture 11 – k-Means and EM Algorithm . of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec11 1 / 57 Copyright By PowCoder代写 加微信 powcoder In the previous lecture, we covered PCA, Autoencoders and Matrix Factorization—all unsupervised learning algorithms. I Each algorithm can be used to approximate high dimensional data […]
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CSC 311: Introduction to Machine Learning Lecture 4 – Linear Models II Roger G. of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec3 1 / 50 Copyright By PowCoder代写 加微信 powcoder More about gradient descent I Choosing a learning rate I Stochastic gradient descent Classification: predicting a discrete-valued target I Binary classification (this week): predicting a
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CSC 311: Introduction to Machine Learning Embedded Ethics — Recommender System Objectives Roger of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec3 1 / 24 Copyright By PowCoder代写 加微信 powcoder Intro ML (UofT) CSC311-Lec3 2 / 24 Today’s lecture is part of the pilot of our new Embedded Ethics initiative. Topic: objective functions for recommender systems
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CSC 311: Introduction to Machine Learning Lecture 2 – Decision Trees & Bias-Variance Decomposition . of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec2 1 / 57 Copyright By PowCoder代写 加微信 powcoder Announcement: HW1 released Decision Trees I Simple but powerful learning algorithm I Used widely in Kaggle competitions I Lets us motivate concepts from information
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Springer Series in Statistics Trevor Tibshirani Jerome Elements of Statistical Learning Data Mining, Inference, and Prediction Copyright By PowCoder代写 加微信 powcoder Second Edition To our parents: Valerie and Vera and Florence and and to our families: Samantha, Timothy, and , Ryan, Julie, and Cheryl Melanie, Dora, Monika, and Ildiko This is page v Printer: Opaque
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Lecture 3, Part 2: Training a Classifier Roger Grosse 1 Introduction Now that we’ve defined what binary classification is, let’s actually train a classifier. We’ll approach this problem in much the same way as we did linear regression: define a model and a cost function, and minimize the cost using gradient descent. The one thing
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Bayesian Reasoning and Machine Learning ⃝c 2007,2008,2009,2010 Notation List Copyright By PowCoder代写 加微信 powcoder dom(x) x=x p(x = tr) p(x = fa) p(x, y) p(x∩y) p(x∪y) p(x|y) I [x = y] pa (x) ch (x) ne (x) X ⊥Y|Z X⊤Y|Z dim x ⟨f (x)⟩p(x) ♯ (x = s, y = t) D a calligraphic symbol
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CSC 311: Introduction to Machine Learning Lecture 3 – Bagging, Linear Models I Roger G. of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec3 1 / 49 Copyright By PowCoder代写 加微信 powcoder Today we will introduce ensembling methods that combine multiple models and can perform better than the individual members. I We’ve seen many individual models
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CSC311 Fall 2021 Homework 2 Homework 2 Deadline: Wednesday, Oct. 13, at 11:59pm. Submission: You need to submit five files through MarkUs1: Copyright By PowCoder代写 加微信 powcoder • Your answers to Questions 1, 2, 3, and 4, as a PDF file titled hw2_writeup.pdf. You can produce the file however you like (e.g. LATEX, Microsoft Word,
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CSC 311: Introduction to Machine Learning Tutorial 10 – EM Algorithm University of Toronto Copyright By PowCoder代写 加微信 powcoder First, brief overview of Expectation-Maximization algorithm. I In the lecture we were using Gaussian Mixture Model fitted with Maximum Likelihood (ML) estimation. Today, practice with the E-M algorithm in an image completion task. We will use
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