CSC 311: Introduction to Machine Learning Lecture 1 – Introduction and Nearest Neighbors . of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec1 1 / 54 Copyright By PowCoder代写 加微信 powcoder This course Broad introduction to machine learning I Algorithms and principles for supervised learning I nearest neighbors, decision trees, ensembles, linear regression, logistic regression, SVMs […]
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Lecture 5: 1 Introduction So far, we’ve only talked about linear models: linear regression and linear binary classifiers. We noted that there are functions that can’t be rep- resented by linear models; for instance, linear regression can’t represent quadratic functions, and linear classifiers can’t represent XOR. We also saw one particular way around this issue:
Image source: cornell.edu clicdata.com University of Toronto Department of Computer Science and Institute CSC311 Embedded Ethics Module Copyright By PowCoder代写 加微信 powcoder Introduction to Embedded Ethics (Online) • The goal of this module is not to tell you what to think about ethical issues, but to make you more comfortable in identifying and discussing them.
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Lecture 3, Part 1: Linear Classification 1 Introduction Last time, we saw an example of a learning task called regression. There, the goal was to predict a scalar-valued target from a set of features. This time, we’ll focus on a slightly different task: binary classification, where the goal is to predict a binary-valued target. Here
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Lecture 10: Mixture modeling , CSC 311 Fall 2020 Learning goals Know what generative process is assumed in a mixture model, and what sort of data it is intended to model Copyright By PowCoder代写 加微信 powcoder Be able to perform posterior inference in a mixture model, in particular – compute the posterior distribution over the
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Lecture 5: Generalization CSC311, Fall 2020 Based on notes by 1 Introduction Copyright By PowCoder代写 加微信 powcoder When we train a machine learning model, we don’t just want it to learn to model the training data. We want it to generalize to data it hasn’t seen before. Fortunately, there’s a convenient way to measure an
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CSC 311: Introduction to Machine Learning Lecture 7 – Probabilistic Models . of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec7 1 / 45 Copyright By PowCoder代写 加微信 powcoder So far in the course we have adopted a modular perspective, in which the model, loss function, optimizer, and regularizer are specified separately. Today we begin putting
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CSC311 Fall 2021 Final Project Final Project Deadline: Friday, Dec. 3, at 11:59pm Submission: You need to submit the following files through MarkUs1: Copyright By PowCoder代写 加微信 powcoder • Your answers to Part A and B, as a PDF file titled final_report.pdf. You can produce the file however you like (e.g. LATEX, Microsoft Word, scanner),
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CSC 311: Introduction to Machine Learning Tutorial 12 – Test 2 Review University of Toronto Copyright By PowCoder代写 加微信 powcoder This tutorial Cover example questions on several topics: Bias-Variance Decomposition Bagging / Boosting Probabilistic Models (Nav ̈e Bayes, Gaussian Discriminant) Principal Component Analysis (Matrix factorization, Autoencoder) K-Means / EM Useful mathematical concepts Working with logs
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CSC 311: Introduction to Machine Learning Lecture 6 – Neural Nets II Roger G. of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec6 1 / 48 Copyright By PowCoder代写 加微信 powcoder Training neural networks with backpropagation Intro ML (UofT) CSC311-Lec6 2 / 48 Recap: Gradient Descent Recall: gradient descent moves opposite the gradient (the direction of
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