Neural network
Machine Learning I
Lecture 5
Taylor series expansion
F ( x ) = F ( x ) +
d ( )
F x
dx
x = x
( x x )
2
1 d
+ -F( x)
2 d x2
2
( x x ) +
x = x
n
1 d ( )
+ -F x
n! d x n
( x x ) n +
x = x
Example
F( x) = e
x
Taylor series of F(x) about x* = 0
Data Science
Generalized Linear
Models: Logistic
Regression
Plan for the Rest of the Semester
Oct. 31st
Generalized Linear Models/Model Fit/Logistic
Regression
Nov. 7th
Speakers Dave Ihrie CTO of CIT Dr Qing Zeng Health
Science Health Informatics Project
DATS 6102
Data Quality Project
Ron Layne
September 27, 2016
Overview
Objective of the project:
To perform a data quality assessment and understand the
impacts of poor data quality.
Upon successful completion of this project, students will:
Perform initia
Steps for creating your data warehouse:
1.
2.
3.
4.
Establish database connections (target and source).
Create the physical target tables for Dimensions, then load.
Add dummy record to Dimensions.
Create the physical target tables for Facts, then load.
Es
Neural network
Machine Learning I
Lecture 4
Brain Function
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The brain
consists
of a large number (approximately 1011 ) of
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highly
connected elements (approximately 104 connections per
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Programming Languages
Machine Learning I
Lecture 2
What is a Programming Language?
A programming language is a formal computer language
designed to communicate instructions to a machine.
communicating
Programming languages can be used to create programs t
Machine Learning Mathematics
Machine Learning I
Lecture 3
Introduction
Machine learning combines statistics and computer science
fields.
Statistics, probability, estimation and confidence intervals are
some of main topics in machine learning in the statis