ECE657A: Data and Knowledge Modeling and Analysis Project Description W2017 There are two broad types of projects: (a) Application-oriented projects: You have a problem, perhaps in your field of research, that you would like to analyze using the concepts and algorithms of this course, and (b) Algorithm-oriented projects: You select an interesting data analysis/data mining […]
Introduction to information system Introduction to Data Science Semester B Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Module Information Title Data Science Code CMP3036M/CMP9063M Semester 2016-2017 Semesters A & B Assessment CMP3036M: Assignment (50%) + Assignment (50%) CMP9063M: Assignment (40%) + Assignment (40%) + Report (20%) Delivery Team in Semester
ECE 657A: Classification – Lecture 5: Classification, Training, Validation and Estimation ECE 657A: Classification Lecture 5: Classification, Training, Validation and Estimation Mark Crowley January 24, 2016 Mark Crowley ECE 657A : Lecture 5 January 24, 2016 1 / 34 Class Admin Announcements Today’s Class Announcements Supervised Learning / Classification Project Pitch Session Break Naive Bayes
University of Waterloo ECE 657A: Data and Knowledge Modeling and Analysis Winter 2017 Assignment 2: Classification and Clustering Due: Sunday March 19, 2017 (by 11:59pm) Assignment Type: group, the max team members is 3. Hand in: one report (PDF) per group, via the LEARN dropbox. Also submit the code / scripts needed to reproduce your
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Decision Trees Mark Broom August 19, 2016 These notes relate to the four equivalent chapters in “Decision Analysis for Management Judgment” by Goodwin and Wright. 1 1 Introduction Complex decisions Management decisions are often complex because they involve: 1. Risk and uncertainty: you may have to choose between various options, with various levels of risk.
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ECE 657A: Data and Knowledge Modelling and Analysis – Lecture 4: Dimensionality Reduction, Probability, Feature Selection ECE 657A: Data and Knowledge Modelling and Analysis Lecture 4: Dimensionality Reduction, Probability, Feature Selection Mark Crowley January 24, 2016 Mark Crowley ECE 657A : Lecture 4 January 24, 2016 1 / 61 Opening Data Example : Guess the
Introduction to information system Introduction to Data Science Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Hello, I’m a Data Scientist! My research interest lies mostly in developing intelligent algorithms and data solutions to the following fields: • Computational advertising: programmatic guarantee • Internet economics and digital products: inventory pricing, information
sms_spamfilter_workshop Data Science Workshop Week 8 – SMS Spam Classification¶ This notebood is based on the “Data Science with Python” notebook from Radmin Rehurek, see http://radimrehurek.com/data_science_python/ The goal of this notebook is to build a working, executable prototype: an app to classify phone SMS messages in English (well, the “SMS kind” of English…) as either
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Introduction to information system Trees and Forests… Gerhard Neumann School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Step back… Bigger Picture Supervised Learning: We want to learn the relation from the input to the output data • Regression: Continuous output labels – Linear regression, Polynomial Regression, kNN (?) • Classification: Discrete / Nominal
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ECE 657A: Classification – Lecture 7: k-NN, SVM and Kernal Methods ECE 657A: Classification Lecture 7: k-NN, SVM and Kernal Methods Mark Crowley February 15, 2017 Mark Crowley ECE 657A : Lecture 7 February 15, 2017 1 / 22 Class Admin Announcements Today’s Class Announcements K-nearest neighbor classification Kernel Methods Support Vector Machines Mark Crowley