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CSE 574 Intro Machine Learning

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  • 5 pages proj1
    • proj1
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Fall 2015
    • Project 1 Report - Machine Learning Devasia Antony Muthalakuzhy Thomas (5016 9054) I. Problem Statement Given a dataset, to find the mean, variance, standard deviation of the individual variables and to find the covariance and correlation matrices for the
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    proj1

  • 16 pages Solutions
    • Solutions
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Fall 2014
    • Name Student No. Midterm Examination, Fall Semester 2014 CSE 4/574: Introduction to Machine Learning, University at Bualo 6:30 PM 7:50 PM Wednesday, October 22, 2014 This examination is worth 100 points divided into two sections: Section A (short question
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    Solutions

  • 26 pages 2Text-Classification
    • 2Text-Classification
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning Srihari Text Classification Sargur Srihari 1 Machine Learning Srihari Information Retrieval Standing Query Ad-hoc retrieval Transient information need Pose one or more queries to a search engine Example Need to track developments in
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    2Text-Classification

  • 8 pages assignment1_old
    • assignment1_old
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE474/574 Introduction to Machine Learning Programming Assignment 1 Handwritten Digits Classification Due Date: March 8th 2017 1 Introduction In this assignment, your task is to implement a Multilayer Perceptron Neural Network and evaluate its performanc
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    assignment1_old

  • 9 pages assignment1(1)
    • assignment1(1)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE474/574 Introduction to Machine Learning Programming Assignment 1 Handwritten Digits Classification Due Date: March 8th 2017 1 Introduction In this assignment, your task is to implement a Multilayer Perceptron Neural Network and evaluate its performanc
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    assignment1(1)

  • 3 pages assignment2
    • assignment2
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE474/574 Introduction to Machine Learning Programming Assignment 2 Classification and Regression Due Date: April 12th 2017 Maximum Score: 100 Note A zipped file containing skeleton Python script files and data is provided. Note that for each problem, yo
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    assignment2

  • 10 pages project1_report
    • project1_report
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Project 1 report Student: Ludovico Ferranti UB#: 5013-5787 mail:[email protected] Problem1 (Closed Form Solution) 1. Model The goal of the first problem was to implement a design matrix and tune the number of basis function that could build the relatio
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    project1_report

  • 6 pages project_1(1)
    • project_1(1)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE474/574: Introduction to Machine Learning(Fall 2014) Instructor: Sargur N. Srihari Teaching Assistants: Zhen Xu, Jun Chu *DRAFT of September 26, 2014* Project 1: Linear Regression with Basis Functions Due Date: Tuesday, Oct. 14th 1 Overview This projec
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    project_1(1)

  • 5 pages hw2(1)
    • hw2(1)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE 474/574 Introduction to Machine Learning Homework Set #2 Problem Sheet (REVISED) Instructor: Sargur N. Srihari Teaching Assistants: Zhen Xu, Jun Chu October 12, 2014 Due Date: October 13th, 2014 1 Instructions: This assignment will be graded in a dier
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    hw2(1)

  • 20 pages BayesianModelComparison(1)
    • BayesianModelComparison(1)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning ! ! ! ! !Srihari Model Selection: Frequentist vs Bayesian Sargur Srihari [email protected] 1 Machine Learning ! ! ! ! !Srihari Topics Regression based on basis functions are determined by M and How to choose them (model complex
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    BayesianModelComparison(1)

  • 30 pages Ch2.2-Gaussian-Distribution
    • Ch2.2-Gaussian-Distribution
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning srihari Gaussian Distribution Sargur N. Srihari 1 Machine Learning srihari The Gaussian Distribution For single real-valued variable x N ( x | , 2 ) = 1 (2 2 )1/ 2 1 exp 2 ( x ) 2 2 Parameters: Mean , variance 2, Carl Friedrich Gauss
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    Ch2.2-Gaussian-Distribution

  • 29 pages Chap4-Part3
    • Chap4-Part3
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Logistic Regression Sargur N. Srihari University at Buffalo, State University of New York USA Machine Learning Srihari Topics in Linear Classification using Probabilistic Discriminative Models 1. Generative vs Discriminative 2. Nonlinear basis funcs in li
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    Chap4-Part3

  • 42 pages Regression-BasisFns(2)
    • Regression-BasisFns(2)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning ! ! ! ! !Srihari Linear Models for Regression Sargur Srihari [email protected] 1 Machine Learning ! ! ! ! !Srihari Linear Regression with One Input Simplest form of linear regression: linear function of single input variable Tas
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    Regression-BasisFns(2)

  • 30 pages 1ML-Overview(1)
    • 1ML-Overview(1)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning Overview Sargur N. Srihari University at Buffalo, State University of New York USA 1 Outline 1. What is Machine Learning (ML)? 2. Types of Problems Solved 1. Regression 2. Classification 3. Responses to Probabilistic Queries 3. Probabilit
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    1ML-Overview(1)

  • 2 pages syllabus-2014
    • syllabus-2014
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE 474/574 Fall 2014 Introduction to Machine Learning http:/www.cedar.buffalo.edu/~srihari/CSE574/index.html M, W 6:30-7:50pm, Davis 101 Description: Machine learning is concerned with the theory, principles and algorithms for constructing programs that
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    syllabus-2014

  • 5 pages project_1
    • project_1
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE474/574: Introduction to Machine Learning(Fall 2014) Instructor: Sargur N. Srihari Teaching Assistants: Zhen Xu, Jun Chu *DRAFT of September 24, 2014* Project 1: Linear Regression with Basis Functions Due Date: Tuesday, Oct. 14th 1 Overview This projec
    • Register Now

    project_1

  • 45 pages BayesianRegression(3)
    • BayesianRegression(3)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning ! ! ! ! !Srihari Bayesian Linear Regression Sargur Srihari [email protected] 1 Machine Learning ! ! ! ! !Srihari Motivation of Bayesian Approach We have seen Linear Regression with M basis functions: y(x, w) = w (x) = w (x) M 1 T
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    BayesianRegression(3)

  • 21 pages EvidenceApproximation(2)
    • EvidenceApproximation(2)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Machine Learning ! ! ! ! !Srihari Evidence Approximation Sargur Srihari [email protected] 1 Machine Learning ! ! ! ! !Srihari Background Bayesian methods need priors with hyperparameters Analytical treatment with distributions over hyper-paramet
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    EvidenceApproximation(2)

  • 28 pages Matlab_tutorial
    • Matlab_tutorial
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • Matlab Tutorial Zhen Xu SUNY at Bualo [email protected] September 8, 2014 Zhen Xu (SUNY at Bualo) Matlab Tutorial September 8, 2014 1 / 28 Overview Introduction to Matlab Basic Matrix Operation Plotting Matlab Programming Zhen Xu (SUNY at Bualo) Matlab Tutor
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    Matlab_tutorial

  • 3 pages hw1(1)
    • hw1(1)
    • SUNY Buffalo
    • Intro Machine Learning
    • CSE 574 - Spring 2014
    • CSE 474/574 Introduction to Machine Learning Homework Set #1 Theory Part Problem Sheet Instructor: Sargur N. Srihari Teaching Assistants: Zhen Xu, Jun Chu August 31, 2014 Due Date: September 8th, 2014 Instructions: This is the problem sheet for Homework1
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    hw1(1)

  • 28 pages Matlab Tutorial
    • Matlab Tutorial
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Matlab Tutorial Sargur N. Srihari SUNY at Bualo [email protected] August 26, 2013 Sargur N. Srihari (SUNY at Bualo) Matlab Tutorial August 26, 2013 1 / 28 Overview Introduction to Matlab Basic Matrix Operation Plotting Matlab Programming Sargur N. S
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    Matlab Tutorial

  • 17 pages 1.2 Curve Fitting
    • 1.2 Curve Fitting
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning Srihari Basic Concepts in Machine Learning Sargur N. Srihari 1 Machine Learning Srihari Introduction to ML: Topics 1.Polynomial Curve Fitting 2.Probability Theory of multiple variables 3.Maximum Likelihood 4.Bayesian Approach 5.Model Sele
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    1.2 Curve Fitting

  • 24 pages 1.5 Information Theory
    • 1.5 Information Theory
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning ! ! ! ! !Srihari Information Theory Sargur N. Srihari 1 Machine Learning ! ! ! ! !Srihari Topics 1. Entropy as an Information Measure 1. Discrete variable definition Relationship to Code Length 2. Continuous Variable Differential Entropy
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    1.5 Information Theory

  • 28 pages 1.1 ML Overview
    • 1.1 ML Overview
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning Overview Sargur N. Srihari University at Buffalo, State University of New York USA 1 Outline 1. What is Machine Learning (ML)? 2. Types of Problems Solved 1. Regression 2. Classification 3. Responses to Probabilistic Queries 3. Probabilit
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    1.1 ML Overview

  • 20 pages 2.1 Probability Distributions
    • 2.1 Probability Distributions
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning Srihari Probability Distributions Sargur N. Srihari 1 Machine Learning Srihari Distributions: Landscape DiscreteBinary Bernoulli DiscreteMultivalued Binomial Beta Multinomial Dirichlet Continuous Gaussian Angular Wishart Students-t Gamma
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    2.1 Probability Distributions

  • 36 pages 3.1 Regression- Basis Functions
    • 3.1 Regression- Basis Functions
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning ! ! ! ! !Srihari Linear Models for Regression Sargur Srihari [email protected] 1 Machine Learning ! ! ! ! !Srihari Overview Plan: Discuss supervised learning starting with regression Goal: predict value of one or more target var
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    3.1 Regression- Basis Functions

  • 22 pages 5.3 Backpropagation
    • 5.3 Backpropagation
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning ! ! ! ! !Srihari Error Backpropagation ! Sargur Srihari ! 1 Machine Learning ! ! ! ! !Srihari Topics ! Neural Network Learning Problem! Need for computing derivatives of Error function! Forward propagation of activations! Backward propaga
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    5.3 Backpropagation

  • 31 pages 5.0 Neural Networks Biological Motivation
    • 5.0 Neural Networks Biological Motivation
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning ! ! ! ! !Srihari Neural Networks ! 1 Machine Learning ! ! ! ! !Srihari Two Groups Of Researchers in Neural Networks ! 1. Study and model biological learning! Network of neurons in the brain provide people with ability to assimilate infor
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    5.0 Neural Networks Biological Motivation

  • 14 pages 5.8 Bayesian Neural Networks
    • 5.8 Bayesian Neural Networks
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning ! ! ! ! !Srihari Bayesian Neural Networks ! Sargur Srihari ! [email protected] ! 1 Machine Learning ! ! ! ! !Srihari Topics discussed here ! 1. Why Bayesian?! 2. Difculty of exact Bayesian treatment and need for approximation! 3.
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    5.8 Bayesian Neural Networks

  • 28 pages 11.2 Basic Sampling
    • 11.2 Basic Sampling
    • SUNY Buffalo
    • Introduction to Machine Learning
    • CSE 574 - Fall 2013
    • Machine Learning ! ! ! ! ! Srihari Basic Sampling Methods Sargur Srihari [email protected] 1 Machine Learning ! ! ! ! ! Srihari Topics 1. Motivation Intractability in ML How sampling can help 2. Ancestral Sampling Using BNs 3. 4. 5. 6. Transformin
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    11.2 Basic Sampling

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    • Profile picture
    Apr 08, 2016
    | Would highly recommend.

    Not too easy. Not too difficult.

    Course Overview:

    Very organized and comprehensive. Weekly quizzes and objective exams.

    Course highlights:

    Introductory ML techniques. Python programming.

    Hours per week:

    6-8 hours

    Advice for students:

    Take the course if you have not taken any ML course. One of the more organized ones.

    • Spring 2016
    • Varun Chandola
    • Great Intro to the Subject Many Small Assignments Group Projects
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