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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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Probability Review for Machine Learning , , and 1 University of Toronto 1Slides adapted from Erdogdu and Zemel Copyright By PowCoder代写 加微信 powcoder Motivation Uncertainty arises through: Noisy measurements Variability between samples Finite size of data sets Probability provides a consistent framework for the quantification and manipulation of uncertainty. Sample Space Sample space Ω is
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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:
Tutorial 4 Problems CSC311, Fall 2021 1 Gradient Descent Intuition Suppose we are trying to optimize the loss function f (x) = 12 xT Ax, where x ∈ R2 4 0 1 Copyright By PowCoder代写 加微信 powcoder 1.LetA= 0 1 andx0= 1 What are the first two iterates of gradient descent, with a learning rate
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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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Linear Algebra Review Problems CSC311, Fall 2021 Commute times 1. Suppose we are trying to predict commute times based on the distance traveled and day of the week. We have the following data: Copyright By PowCoder代写 加微信 powcoder dist day commute time 2.71 25 3.41 31 5.22 45 1.03 16 2.85 22 (a) We estimate
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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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CSC311 Fall 2021 Tutorial 3 Tutorial 3 Exercises 1. Bias, Variance, and Bayes Error. The purpose of this exercise is to show a simple example where you can compute the bias, variance, and Bayes error of a predictor. For this question, we assume we have N scalar-valued observations {x(i)}Ni=1 sampled independently from a Gaussian distribution
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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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