CSE 130 Final Solution, Spring 2018 Nadia Polikarpova June 11, 2018 Q1: Lambda Calculus: Sets [20 pts] 1.1 Empty set [5 pts] let EMPTY = \x -> FALSE 1.2 Insert an element [5 pts] let INSERT = \n s x -> ITE (EQL n x) TRUE (s x) Alternatively: let INSERT = \n s x […]
CS计算机代考程序代写 prolog Lambda Calculus decision tree CSE 130 Final Solution, Spring 2018 Read More »
The University of Hong Kong Department of Electrical and Electronic Engineering ELEC6008 Pattern recognition and machine learning (2020-2021) Written Assignment: 100 marks (Counts towards 15% of the overall assessment) 1. Submission Format and Deadline Solution addressing the problems should be submitted in PDF format and uploaded to the Moodle System by Apr 14, 2021 23:55
Machine Learning 1 TU Berlin, WiSe 2020/21 Decision Trees, Random Forests, Boosting (40 P) The goal of this homework is to extend decision trees, using (1) random forests or (2) boosting. For this, we will make use of an existing decision tree implementation (available in scikit-learn), that we can then reuse for implementing the two
CS计算机代考程序代写 decision tree algorithm Machine Learning 1 TU Berlin, WiSe 2020/21 Read More »
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
Wojciech Samek & Gr¨goire Montavon ML1 Lecture 9: Decision Trees & Random Forests 2 ML1 Lecture 9: Decision Trees & Random Forests 3 ML1 Lecture 9: Decision Trees & Random Forests 4 ML1 Lecture 9: Decision Trees & Random Forests 5 ML1 Lecture 9: Decision Trees & Random Forests 6 ML1 Lecture 9: Decision Trees
CS计算机代考程序代写 decision tree Wojciech Samek & Gr¨goire Montavon Read More »
Machine Learning 1, Course Outline � Covered topics � Bayes Decision Theory, Parameter Estimation � Component/Discriminant Analyses (PCA, Fisher) � Model Selection, Learning Theory � SVMs, Regression, Neural Nets, Boosting, Decision Trees � Clustering, Latent Variable Models � Weekly homeworks � Elective course (only for Machine Learning 1-X) 1/29 Machine Learning 1, Important Dates Registering
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Machine Learning for Financial Data December 2020 FEATURE ENGINEERING (CONCEPTS – PART 3) Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Feature Engineering Contents ◦ Feature Selection ◦ Filter-based Feature Selection ◦ Feature Selection using Pearson’s Correlation ◦ Feature Selection using Hypothesis Testing ◦ Feature Transformation ◦ Feature Transformation using Principal Component Analysis
Machine Learning for Financial Data December 2020 FEATURE ENGINEERING (CONCEPTS – PART 2) Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Feature Engineering Contents ◦ Feature Improvement ◦ Feature Construction Feature Improvement Data Probability Distribution Variable Transformations ▪ Linear and logistic regression assume that the variables are normally distributed ▪ If they are
Machine Learning for Financial Data January 2021 CLASSIFICATION (CONCEPTS – PART 1) Contents ◦ The Iris Dataset ◦ Training & Testing Data Partitioning ◦ Evaluation by Accuracy Score ◦ k-Nearest Neighbours Classifier ◦ Decision Tree Classifier Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Classification The Iris Dataset The Iris dataset has a
CS计算机代考程序代写 algorithm decision tree python Machine Learning for Financial Data Read More »
Machine Learning for Financial Data February 2021 CLASSIFICATION (CONCEPTS – PART 2) Contents ◦ Naive Bayes Classifier ◦ Support Vector Machine (SVM) ◦ Random Forest ◦ Logistic Regression Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Classification Naive Bayes Classifier Naïve Bayes classifier relies on the probability function of pedal & sepal measures