C Chegg Search X Bike Rides and the Poisson Mode X WILL Poisson Bike Rides X Dashboard X + V X ( > C A pantelis.github.io/data-mining/aiml-common/assignments/poisson-regression/index.html INTRODUCTION TO DATA MINING Contents Course Introduction Data Science 360 Maximum Likelihood I (50 points) Bike Rides and the Poisson Model Maximum Likelihood II (50 points) ML Pipelines A Case Study of an ML Architecture - Uber To help the urban planners, you are called to model the daily bike rides in NYC using this dataset. The dataset contains date, day of the week, high and low temp, precipitation and THE LEARNING PROBLEM bike ride counts as columns. The Learning Problem Linear Regression Maximum Likelihood (ML) Estimation Maximum Likelihood I (50 points) Entropy The obvious choice in distributions is the Poisson distribution which depends only on one Stochastic Gradient Descent parameter, ), which is the average number of occurrences per interval. We want to estimate Introduction to Classification this parameter using Maximum Likelihood Estimation. CLASSICAL LEARNING METHODS Implement a Gradient Descent algorithm from scratch that will estimate the Poisson Logistic Regression distribution according to the Maximum Likelihood criterion. Plot the estimated mean vs Decision Trees iterations to showcase convergence towards the true mean. Regression tree stumps Ensemble Methods References: Random Earacts c17931a3f000492c....zip Show all X 9. C ENG 06:05 1 Raining now IN 04-10-2022C Chegg Search X Bike Rides and the Poisson Mode X WILL Poisson Bike Rides X Dashboard X + V X ( > C A pantelis.github.io/data-mining/aiml-common/assignments/poisson-regression/index.html INTRODUCTION TO DATA MINING Contents Course Introduction bike ride counts as columns. Data Science 360 Maximum Likelihood I (50 points) Maximum Likelihood II (50 points) ML Pipelines A Case Study of an ML Architecture - Maximum Likelihood I (50 points) Uber The obvious choice in distributions is the Poisson distribution which depends only on one THE LEARNING PROBLEM parameter, ), which is the average number of occurrences per interval. We want to estimate The Learning Problem this parameter using Maximum Likelihood Estimation. Linear Regression Maximum Likelihood (ML) Estimation Implement a Gradient Descent algorithm from scratch that will estimate the Poisson Entropy distribution according to the Maximum Likelihood criterion. Plot the estimated mean vs Stochastic Gradient Descent iterations to showcase convergence towards the true mean. Introduction to Classification References: CLASSICAL LEARNING METHODS This blog post. This blog post and note the negative log likelihood function. Logistic Regression Decision Trees # Code here Regression tree stumps Ensemble Methods Random Earacts Mavimum Lilzaliband 11 (En naintel c17931a3f000492c....zip Show all X 9. C O ENG 06:05 1 Raining now IN 04-10-2022C Chegg Search X Bike Rides and the Poisson Mode X WILL Poisson Bike Rides X Dashboard X + V X C A pantelis.github.io/data-mining/aiml-common/assignments/poisson-regression/index.html INTRODUCTION TO DATA MINING Contents Course Introduction Data Science 360 Maximum Likelihood I (50 points) Maximum Likelihood II (50 points) ML Pipelines A Case Study of an ML Architecture - Maximum Likelihood II (50 points) Uber A colleague of yours suggest that the parameter > must be itself dependent on the weather THE LEARNING PROBLEM and other factors since people bike when its not raining. Assume that you model _ as The Learning Problem di = exp(w xi) Linear Regression Maximum Likelihood (ML) Estimation where X; is one of the example features and w is a set of parameters. Entropy Train the model with SGD with this assumption and compare the MSE of the predictions Stochastic Gradient Descent with the Maximum Likelihood I approach. Introduction to Classification You may want to use this partial derivative of the log likelihood function CLASSICAL LEARNING METHODS Logistic Regression # Decision Trees Regression tree stumps