This document provides an overview of data preparation and descriptive statistics in SystemML. It discusses data formatting, pre-processing such as transforming categorical features and handling missing values, and descriptive statistics including univariate, bivariate, and stratified statistics. Univariate statistics describe the distribution of individual variables, bivariate statistics measure associations between pairs of variables, and stratified statistics measure associations between variables within subgroups defined by a categorical variable to control for confounding.
Regression using Apache SystemML by Alexandre V EvfimievskiArvind Surve
This deck will present regression algorithms Linear Regression -- Least Square, Direct solve -- , Conjugate Gradient, and Generalized Linear Model supported in Apache SystemML
Classification using Apache SystemML by Prithviraj SenArvind Surve
This deck will cover various algorithms at high level. Those algorithms include "Supervised Learning and Classification", "Training Discriminative Classifiers", "Representer Theorem", "Support Vector Machines", "Logistic Regression", "Generative Classifiers: Naive Bayes", "Deep Learning" and "Tree Ensembles"
Overview of Apache SystemML by Berthold Reinwald and Nakul JindalArvind Surve
This deck will provide SystemML architecture, how to get documentation for usage, algorithms etc. It will explain usage of it through command line or through notebook.
Regression using Apache SystemML by Alexandre V EvfimievskiArvind Surve
This deck will present regression algorithms Linear Regression -- Least Square, Direct solve -- , Conjugate Gradient, and Generalized Linear Model supported in Apache SystemML
Classification using Apache SystemML by Prithviraj SenArvind Surve
This deck will cover various algorithms at high level. Those algorithms include "Supervised Learning and Classification", "Training Discriminative Classifiers", "Representer Theorem", "Support Vector Machines", "Logistic Regression", "Generative Classifiers: Naive Bayes", "Deep Learning" and "Tree Ensembles"
Overview of Apache SystemML by Berthold Reinwald and Nakul JindalArvind Surve
This deck will provide SystemML architecture, how to get documentation for usage, algorithms etc. It will explain usage of it through command line or through notebook.
30-minute talk from Spark Summit East about the internals of Apache SystemML. Apache SystemML is a system that automatically parallelizes machine learning algorithms, greatly improving the productivity of data scientists. For more information about Apache SystemML, please go to the project's home page at http://systemml.apache.org
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This session includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
Clustering and Factorization using Apache SystemML by Prithviraj SenArvind Surve
This deck will discuss application of Matrix Factorization in Machine Learning. It will discuss Least Square Matrix Factorization, Poisson Matrix Factorization.
Apache SystemML Architecture by Niketan PanesarArvind Surve
This deck will present high level Apache SystemML design and architecture containing language, compiler and runtime modules. It will describe how compilation chain gets generated and variable analysis done. It will show HOPs and runtime plan for sample use case. It will show how to get statistics, and some diagnostic tools can be used.
Slides from the talk at Open Data Science Conference London 2017 (http://odsc.com/london)
The presentation is using R language to show how to tackle the Machine Learning tasks.
Data mining Basics and complete description Sulman Ahmed
This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results
30-minute talk from Spark Summit East about the internals of Apache SystemML. Apache SystemML is a system that automatically parallelizes machine learning algorithms, greatly improving the productivity of data scientists. For more information about Apache SystemML, please go to the project's home page at http://systemml.apache.org
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This session includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
Clustering and Factorization using Apache SystemML by Prithviraj SenArvind Surve
This deck will discuss application of Matrix Factorization in Machine Learning. It will discuss Least Square Matrix Factorization, Poisson Matrix Factorization.
Apache SystemML Architecture by Niketan PanesarArvind Surve
This deck will present high level Apache SystemML design and architecture containing language, compiler and runtime modules. It will describe how compilation chain gets generated and variable analysis done. It will show HOPs and runtime plan for sample use case. It will show how to get statistics, and some diagnostic tools can be used.
Slides from the talk at Open Data Science Conference London 2017 (http://odsc.com/london)
The presentation is using R language to show how to tackle the Machine Learning tasks.
Data mining Basics and complete description Sulman Ahmed
This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results.This course is all about the data mining techniques and how we mine the data and get optimize results
Data Mining DataLecture Notes for Chapter 2IntroducOllieShoresna
Data Mining: Data
Lecture Notes for Chapter 2
Introduction to Data Mining
by
Tan, Steinbach, Kumar
What is Data?Collection of data objects and their attributes
An attribute is a property or characteristic of an objectExamples: eye color of a person, temperature, etc.Attribute is also known as variable, field, characteristic, or featureA collection of attributes describe an objectObject is also known as record, point, case, sample, entity, or instance
Attributes
Objects
Attribute ValuesAttribute values are numbers or symbols assigned to an attribute
Distinction between attributes and attribute valuesSame attribute can be mapped to different attribute values Example: height can be measured in feet or meters
Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are integers But properties of attribute values can be different
ID has no limit but age has a maximum and minimum value
Types of Attributes There are different types of attributesNominalExamples: ID numbers, eye color, zip codesOrdinalExamples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short}IntervalExamples: calendar dates, temperatures in Celsius or Fahrenheit.RatioExamples: temperature in Kelvin, length, time, counts
Properties of Attribute Values The type of an attribute depends on which of the following properties it possesses:Distinctness: = Order: < > Addition: + - Multiplication: * /
Nominal attribute: distinctnessOrdinal attribute: distinctness & orderInterval attribute: distinctness, order & additionRatio attribute: all 4 properties
Attribute Type
Description
Examples
Operations
Nominal
The values of a nominal attribute are just different names, i.e., nominal attributes provide only enough information to distinguish one object from another. (=, )
zip codes, employee ID numbers, eye color, sex: {male, female}
mode, entropy, contingency correlation, 2 test
Ordinal
The values of an ordinal attribute provide enough information to order objects. (<, >)
hardness of minerals, {good, better, best},
grades, street numbers
median, percentiles, rank correlation, run tests, sign tests
Interval
For interval attributes, the differences between values are meaningful, i.e., a unit of measurement exists.
(+, - )
calendar dates, temperature in Celsius or Fahrenheit
mean, standard deviation, Pearson's correlation, t and F tests
Ratio
For ratio variables, both differences and ratios are meaningful. (*, /)
temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical current
geometric mean, harmonic mean, percent variation
Attribute Level
Transformation
Comments
Nominal
Any permutation of values
If all employee ID numbers were reassigned, would it make any difference?
Ordinal
An order preserving change of values, i.e.,
new_value = f(old_value)
where f is a monotonic function.
An attribut ...
Apache SystemML Optimizer and Runtime techniques by Matthias BoehmArvind Surve
This deck describes general framework techniques for Large Scale Machine Learning systems. It explains Apache SystemML specific Optimizer and Runtime techniques. It will describe data structures, DAG compilation, operator selection including fused operators, dynamic recompilation, inter procedure analysis and some ongoing research projects.
Apache SystemML Architecture by Niketan PanesarArvind Surve
This deck will present high level Apache SystemML design and architecture containing language, compiler and runtime modules. It will describe how compilation chain gets generated and variable analysis done. It will show HOPs and runtime plan for sample use case. It will show how to get statistics, and some diagnostic tools can be used.
Clustering and Factorization using Apache SystemML by Prithviraj SenArvind Surve
This deck will discuss application of Matrix Factorization in Machine Learning. It will discuss Least Square Matrix Factorization, Poisson Matrix Factorization.
Classification using Apache SystemML by Prithviraj SenArvind Surve
This deck will cover various algorithms at high level. Those algorithms include "Supervised Learning and Classification", "Training Discriminative Classifiers", "Representer Theorem", "Support Vector Machines", "Logistic Regression", "Generative Classifiers: Naive Bayes", "Deep Learning" and "Tree Ensembles"
Data preparation, training and validation using SystemML by Faraz Makari Mans...Arvind Surve
This deck will provide you an information related to data preparation, training, testing and validation of data used in Machine Learning using Apache SystemML. As well as it will provide Descriptive statistics -- Univariate Statistics, Bivariate Statistics and Stratified Statistics.
Overview of Apache SystemML by Berthold Reinwald and Nakul JindalArvind Surve
This deck will provide SystemML architecture, how to get documentation for usage, algorithms etc. It will explain usage of it through command line or through notebook.
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This deck includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
Apache SystemML Optimizer and Runtime techniques by Matthias BoehmArvind Surve
This deck describes general framework techniques for Large Scale Machine Learning systems. It explains Apachhe SystemML specific Optimizer and Runtime techniques. It will describe data structures, DAG compilation, operator selection including fused operators, dynamic recompilation, inter procedure analysis and some ongoing research projects.
Regression using Apache SystemML by Alexandre V EvfimievskiArvind Surve
This deck will present regression algorithms Linear Regression -- Least Square, Direct solve -- , Conjugate Gradient, and Generalized Linear Model supported in Apache SystemML
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
8. Signature of transform()
§ Invocation 1:
§ Resulting metadata: # distinct values in categorical columns, list of distinct values with their
recoded IDs, number of bins, bin width, etc.
§ An existing transformation can be applied to new data using the metadata generated in an
earlier invocation
§ Invocation 2:
8
output = transform (target = input,
spec = specification,
transformPath = "/path/to/metadata“);
output = transform (target = input,
transformPath = "/path/to/new_metadata“
applyTransformPath = "/path/to/metadata“);
11. Pre-Processing Training and
Testing Data
Training phase
Testing phase
11
Train = read ("/user/ml/trainset.csv");
Spec = read("/user/ml/tf.spec.json“, data_type = "scalar",
value_type = "String");
trainD = transform (target = Train,
transformSpec = Spec,
transformPath = "/user/ml/train_tf_metadata");
# Build a predictive model using trainD
...
Test = read ("/user/ml/testset.csv");
testD = transform (target = Test,
transformPath = "/user/ml/test_tf_metadata",
applyTransformPath = "/user/ml/train_tf_metdata");
# Test the model using testD
...
12. Cross Validation
K-fold Cross Validation:
1. Shuffle the data points
2. Divide the data points into 𝑘 folds of (roughly)
the same size
3. For 𝑖 = 1, … , 𝑘:
• Train each model on all the data points that
do not belong to fold 𝑖
• Test each model on all the examples in fold 𝑖
and compute the test error
4. Select the model with the minimum average test
over all 𝑘 folds
5. (Train the winning model on all the data points)
12
Testing Training
Example: 𝑘 = 5
14. Univariate Statistics
14
Row Name of Statistic Scale Category
1 Minimum +
2 Maximum +
3 Range +
4 Mean +
5 Variance +
6 Standard deviation +
7 Standard error of mean +
8 Coefficient of variation +
9 Skewness +
10 Kurtosis +
11 Standard error of skewness +
12 Standard error of Kurtosis +
13 Median +
14 Intequartilemean +
15 Number of categories +
16 Mode +
17 Number of modes +
Central tendency measures
Dispersion measures
Shape measures
Categorical measures
20. Nominal-vs-Scale Statistics
𝐹 statistic
§ A measure for the strength of association between a categorical feature and a scale
feature
§ Assumptions (𝑥 categorical, 𝑦 scale):
§ 𝑦 ~ 𝑁𝑜𝑟𝑚𝑎𝑙 𝜇, 𝜎)
- same variance for all 𝑥
§ 𝑥 has small value domain with large frequency counts, 𝑥A non-random
§ All records are iid
§ Under independence assumption 𝐹 distributed approximately 𝐹(𝑘 − 1, 𝑛 − 𝑘)
20
𝐹 =
∑ 𝑓𝑟𝑒𝑞 𝑥 𝑦B 𝑥 − 𝑦k )/(𝑘 − 1)5
∑ 𝑦A − 𝑦B 𝑥A
)/(𝑛 − 𝑘)C
AD0
=
𝜂)(𝑛 − 𝑘)
1 − 𝜂)(𝑘 − 1)
ESS: Explained Sum of Squares
RSS
Degrees of freedom
Degrees of freedom