STT 301 Homework Assignment 2 STT 301 Homework Assignment 2 Shawn Santo September 25, 2017 Homework Assignment 2 is due Wednesday, October 4 at 11:00pm EST. Instructions and Rubric You must complete this individual homework assignment using R Markdown. You may modify this file to include your solutions. However, please be sure it only contains […]
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Introduction to information system Naïve Bayes and Decision Tree Deema Abdal Hafeth CMP3036M/CMP9063M Data Science 2016 – 2017 Objectives Naïve Bayes Naïve Bayes and nominal attributes Bayes’s Rule Naïve Bayes and numeric attributes Decision Tree Information value (entropy) Information Gain From Decision Tree to Decision Rule •
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Introduction to information system Hypothesis Testing Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Assessment Item 1 Has Been Released Please check the assessment documents on Blackboard, including: • Tasks/questions • Datasets (ds_training.csv, ds_test.csv, ds_submission_sample.csv) • Hand-in date • Submission requirements! Note: • Several algorithms and methods will be delivered in
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Introduction to information system Probability Review Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Today’s Objectives • Basic Concepts in Set Theory and Probability Theory • Kolmogorov’s Axioms • Random Variable • Conditional Probability • Bayes’ Rule • Expectation and Variance • Appendix: Two Popular Inequalities Have you ever made a
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CMP3036M Data Science, page 1 of 4 University of Lincoln School of Computer Science 2016 – 2017 Assessment Item 2 of 2 Briefing Document Title: CMP3036M Data Science Indicative Weighting: 50% Learning Outcomes On successful completion of this component a student will have demonstrated competence in the following areas: LO1 Critically apply fundamental concepts
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Introduction to information system Introduction to R Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop What is R? • R is a free software environment for statistical computing and graphics. • R compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. • R
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Introduction to information system Model Evaluation Metrics Deema Hafeth and JingMin Huang School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today‟s Objectives • Do the Exercises 1-3 • There are several hints about using the relevant R packages and built-in functions. You can google and read the materials in
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Introduction to information system Topic Models for Information Retrieval Gerhard Neumann School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science The poor feedbag Feed the feedbag! • It is starving… almost dead… • Feedback (through the „backdoors“): – Too much math! • Yes… its data science. But we reduced the math level significantly. –
Introduction to information system Linear Algebra Gerhard Neumann School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Today‘s Agenda! • Make you remember Linear Algebra • Mostly easy but we probably have forgotten it • Introduction to: – Vectors – Matrices – Matrix Calculus Revisiting Linear Regression Why do you hate us??? • Uff…
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Introduction to information system Linear Regression Bowei Chen, Deema Hafeth and Jingmin Huang School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objectives • Study the slides in Part I, including: – Implementation of linear regression in R – Interpretation of results of linear regression in R • Do
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