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CS 2750  Machine Learning  Pittsburgh Study Resources
 University Of Pittsburgh (Pittsburgh, Pitt)
 Hauskrecht, M
 Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook (Advanced Textbooks in Control and Signal Processing), Modelling and Optimization of Distributed Parameter Systems, Nonlinear Dimensionality Reduction (Information Science and Statistics), Logistic Regression Using the SAS System: Theory and Application

Lecture Notes A On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x48845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 5329 Se

Lecture Notes On Support Vector Machines
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 10 Support vector machines Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Fisher Linear Discriminant Algorithms for linear decision boundary Support vector machines Max

Lecture Notes On Classification Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 9 Classification learning II Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Logistic regression model Defines a linear decision boundary Discriminant functions: g1 (x ) g (w T x ) g 0 (x)

Lecture Notes On Inference And Learnin
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 15 Bayesian belief networks: Inference and learning. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Midterm exam When: Tuesday, March 4 , 2014 Midterm is: Inclass (75 minutes) closed boo

Lecture Notes On Linear Regression
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 7 Linear regression Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Linear regression Function f : X Y is a linear combination of input components d f ( x) w0 w1 x1 w2 x 2 wd x d w0 w j x j

Lecture Notes H On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 11 SVMs for regression Multilayer neural networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Linearly nonseparable case Allow some flexibility on crossing the separating hyperplane CS

Lecture Notes G On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 8 Classification learning: Logistic regression Generative classification model Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Classification Data: D cfw_d1 , d 2 ,., d n d i x i , yi

Lecture Notes On Bayesian Belief Network
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 14 Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Density estimation Data: D cfw_D1 , D2 ,., Dn Di x i a vector of attribute values Attributes: modeled by random

Lecture Notes E On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 5 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Density estimation: Maximum likelihood (ML) Bayesian parameter estimates MAP Bernoulli distribution

Lecture Notes I On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 12 Nonparametric classification methods Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Nonparametric vs Parametric Methods Nonparametric models: More flexibility no parametric model is ne

Lecture Notes B On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 2 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x48845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Types of learning Supervised learning Learning mapping between inpu

Lecture Notes C On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 3 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Announcements Homework 1: due on Thursday, January 23 before the class You should submit: A hardcopy of the report (bef

Lecture Notes F On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 6 Nonparametric density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Parametric density estimation Parametric density estimation: A set of random variables X cfw_ X 1 , X 2 ,

Lecture Notes D On Machine Learning
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 4 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Density estimation Data: D cfw_D1 , D2 ,., Dn Di x i a vector of attribute values Objective: try to estimate the underly

Lecture Notes On Multiclass Classification Decision Trees
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 13 Multiclass classification Decision trees Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Midterm exam Midterm Tuesday, March 4, 2014 Inclass (75 minutes) closed book material covered