C Chegg Search X Dashboard X Bike Rides and the Poisson Mode X WILL Poisson Bike Rides X + V X -> C A pantelis.github.io/data-mining/aiml-common/assignments/poisson-regression/index.html E Contents Maximum Likelihood I (50 points) Bike Rides and the Poisson Model Maximum Likelihood II (50 points) To help the urban planners, you are called to model the daily bike rides in NYC using this Data Mining dataset. The dataset contains date, day of the week, high and low temp, precipitation and bike ride counts as columns. Q Search the docs ... SYLLABUS Maximum Likelihood I (50 points) Syllabus The obvious choice in distributions is the Poisson distribution which depends only on one parameter, A, which is the average number of occurrences per interval. We want to estimate INTRODUCTION TO DATA MINING this parameter using Maximum Likelihood Estimation. Course Introduction Data Science 360 Implement a Gradient Descent algorithm from scratch that will estimate the Poisson ML Pipelines distribution according to the Maximum Likelihood criterion. Plot the estimated mean vs A Case Study of an ML Architecture - iterations to showcase convergence towards the true mean. Uber References: THE LEARNING PROBLEM The Learning Problem This blog post. This blog post and note the negative log likelihood function. Linear Regression # Code here 10 C Raining now O ENG 05:50 IN 04-10-2022C Chegg Search X Dashboard X Bike Rides and the Poisson Mode X WILL Poisson Bike Rides X + V X -> C A pantelis.github.io/data-mining/aiml-common/assignments/poisson-regression/index.html Contents THE MIVY DUDE THIRD VIVY PUSE UIT INVIL Maximum Likelihood I (50 points) Maximum Likelihood II (50 points) # Code here Data Mining Maximum Likelihood II (50 points) Q Search the docs ... A colleague of yours suggest that the parameter / must be itself dependent on the weather and other factors since people bike when its not raining. Assume that you model ) as SYLLABUS Syllabus di = exp(w xi) INTRODUCTION TO DATA MINING where Xi is one of the example features and w is a set of parameters. Course Introduction Train the model with SGD with this assumption and compare the MSE of the predictions Data Science 360 with the Maximum Likelihood I approach. ML Pipelines A Case Study of an ML Architecture - You may want to use this partial derivative of the log likelihood function Uber # THE LEARNING PROBLEM The Learning Problem Previous Linear Regression Probability Assignment 10 C O ENG 05:50 Raining now IN 04-10-2022