PPT-Classification / Regression

Neural Networks 2 Neural networks Topics Perceptrons structure training expressiveness Multilayer networks possible structures activation functions training with gradient descent and . Methods for Dummies. Isobel Weinberg & Alexandra . Westley. Student’s t-test. Are these two data sets significantly different from one another? . William Sealy Gossett. Are these two distributions different?. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistics and Data Analysis. Part . 6 – Regression Model-1. Conditional Mean . U.S. Gasoline Price. Design. Basics. Two potential outcomes . Yi(0) . and. Yi(1), . causal effect . Yi(1) − Yi(0), . binary treatment indicator . Wi. , . covariate. Xi, . and the observed outcome equal to:. At . Xi = c . Greg Cox. Richard Shiffrin. Continuous response measures. The problem. What do we do if we do not know the functional form?. Rasmussen & Williams, . Gaussian Processes for Machine Learning. http://www.gaussianprocesses.org/. SIT095. The Collection and Analysis of Quantitative Data II. Week 9. Luke Sloan. Introduction. Recap – Last Week. Workshop Feedback. Multinomial Logistic Regression in SPSS. Model Interpretation. In Class Exercise. Linear Regression. Section 3.2. Reference Text:. The Practice of Statistics. , Fourth Edition.. Starnes, Yates, Moore. Warm up/ quiz . Draw a quick sketch of three scatterplots:. Draw a plot with r . Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . Stat-GB.3302.30, UB.0015.01. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistical Inference and Regression Analysis. Part 0 - Introduction. . Professor William Greene; Economics and IOMS Departments. Weifeng Li, Sagar . Samtani. and . Hsinchun. . Chen. Spring 2016. Acknowledgements:. Cynthia . Rudin. , Hastie & . Tibshirani. Michael Crawford – San Jose State University. Pier Luca . Lanzi. In linear regression, the assumed function is linear in the coefficients, for example, . .. Regression is nonlinear, when the function is a nonlinear in the coefficients (not x), e.g., . T. he most common use of nonlinear regression is for finding physical constants given measurements.. Ryan Shaun . Joazeiro. de Baker. Prediction. Pretty much what it says. A student is using a tutor right now.. Is he gaming the system or not?. . (“attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material”). Please sit down if you:. Are taller than 5’9”. Have blonde Hair . Have brown Eyes. Are left-Handed. Why Classify?. To study the diversity of life, biologists use a . classification . system to name organisms and group them in a logical manner. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.