EECS 3101 York University Instructor: Andy Mirzaian MACHINE MODEL AND TIMING ANALYSIS NOTATION Introduction This course has two major goals. (1) To teach certain fundamental combinatorial (as opposed to numerical) algorithms. (2) To teach general techniques for the design and analysis of algorithms. The first question to address is “What is analysis of algorithms?”. We […]
EECS 3101 Prof. Andy Mirzaian Welcome to the beautiful and wonderful world of algorithms! 2 STUDY MATERIAL: • [CLRS] chapter 1 • Lecture Note 1 NOTE: • Material covered in lecture slides are as self contained as possible and may not necessarily follow the text book format. 3 Origin of the word Algorithm =
The pedagogical purpose of this assignment is to help you develop the following skills: Work with text data. Prepare raw text data for machine learning tasks using the Natural Language Tool Kit (NLTK). Experiment with new classifiers in addition to decision trees and multi-layer perceptrons: Naïve Bayes, k-Nearest Neighbors, Support Vector Machines,
CS61B Lecture #31 • More balanced search structures (DS(IJ), Chapter 9 Coming Up: • Pseudo-random Numbers (DS(IJ), Chapter 11) Last modified: Thu Nov 1 19:39:39 2018 Copyright By PowCoder代写 加微信 powcoder CS61B: Lecture #31 1 Really Efficient Use of Keys: the Trie • Haven’t said much about cost of comparisons. • For strings, worst case
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Ensemble methods Data Mining Prof. Dr. Matei Demetrescu Statistics and Econometrics (CAU Kiel) Summer 2020 1 / 39 Moving further away from classical statistics So far, we proceeded as follows: 1 get (many) data, then 2 make a single – typically complex – predictor. 3 Don’t forget validating and testing the prediction model. We’ve also
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Data mining Institute of statistics and econometrics (University of Kiel) June 1, 2020 Contents Preliminaries 1 1 Statistical learning 3 1.1 Fromstatisticstostatisticallearning …………………. 3 1.2 Supervisedlearning………………………….. 4 1.3 Unsupervisedlearning ………………………… 5 2 Supervised learning: some background 6 2.1 Errorquantification………………………….. 6 2.2 Learningforprediction………………………… 10 2.3 Leaningwithmanyfeatures ……………………… 12 3 Linear prediction and classification 14 3.1 Predictionwithlinearregression…………………….
Prof. Dr. Matei Demetrescu University of Kiel Institute for Statistics and Econometrics Summer 2020 Data Mining Course description The course provides a statistical introduction to methods designed for analyzing large and complex data sets and relations. The focus is on regression and classification methods. We start in a parametric setup with linearity, but move on
Getting nonlinear Data Mining Prof. Dr. Matei Demetrescu Statistics and Econometrics (CAU Kiel) Summer 2020 1 / 40 Get more out of the data? We used linearity as a starting point rather than truth carved in stone. When a linear approximation is not good enough,1 some alternative approaches may offer a lot of flexibility, without
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CS 189/289A Introduction to Machine Learning Spring 2021 Final • The exam is open book, open notes for material on paper. On your computer screen, you may have only this exam, Zoom, a limited set of PDF documents (see Piazza for details), and four browser windows/tabs: Gradescope, the exam instructions, clarifications on Piazza, and the
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Lecture 23: Ensembles of trees CS 189 (CDSS offering) 2022/03/18 Today’s lecture Copyright By PowCoder代写 加微信 powcoder Today, we will discuss the concept of ensembling: combining many “weak” We will use decision trees as the vessel for studying this concept We will focus on two types of ensembling: bootstrap aggregation (“bagging”) and boosting — these
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