Midterm 2 A CSC311 Fall 2020 27 Midterm 2 A 1. 6 To get full marks in each case provide a brief explanation: Grading Notes. 1 mark for correct true/false, 1 mark for explanation. Copyright By PowCoder代写 加微信 powcoder (a) We apply PCA to reduce the number of features of a linearly separable, binary classification […]
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CSC 311: Introduction to Machine Learning Lecture 9 – PCA, Matrix Completion, Autoencoders Roger G. of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec9 1 / 50 Copyright By PowCoder代写 加微信 powcoder So far in this course: supervised learning Today we start unsupervised learning ▶ No labels, so the purpose is to find patterns in data
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Lecture 6: Backpropagation Roger Grosse 1 Introduction So far, we’ve seen how to train “shallow” models, where the predictions are computed as a linear function of the inputs. We’ve also observed that deeper models are much more powerful than linear ones, in that they can compute a broader set of functions. Let’s put these two
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Lecture 2: Linear regression 1 Introduction Let’s jump right in and look at our first machine learning algorithm, linear regression. In regression, we are interested in predicting a scalar-valued target, such as the price of a stock. By linear, we mean that the target must be predicted as a linear function of the inputs. This
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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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CSC 311: Introduction to Machine Learning Lecture 12 – Reinforcement Learning . of Toronto, Fall 2021 Intro ML (UofT) CSC311-Lec12 1 / 60 Copyright By PowCoder代写 加微信 powcoder Reinforcement Learning Problem Recall: we categorized types of ML by how much information they provide about the desired behavior. Supervised learning: labels of desired behavior Unsupervised learning:
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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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