Valencian Summer School 2015
Day 1
Lecture 1
State of the Art in Machine Learning
Poul Petersen (BigML)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
In Data Structure, a 2–3 tree is a tree data structure, where every node with children (internal node) has either two children (2-node) and one data element or three children (3-nodes) and two data elements.
In Data Structure, a 2–3 tree is a tree data structure, where every node with children (internal node) has either two children (2-node) and one data element or three children (3-nodes) and two data elements.
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
The PPT would provide the Database Normalization is to restructure the logical data model of a database to:
Eliminate Redundancy
Organize Data Efficiently
Reduce the potential for Data Anomalies.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Here all UML diagrams for
ATM system, Library Managing System,
Online Banking System are posted refer it for UML/OOAD lab record (jntu B.tech 4-1 IT/CSE)
images not set properly dowload it and reduse the image size to view full images.
Students will be able to learn the various kinds of searching, sorting and hashing techniques to handle the data structures efficiently. This PPT contains the following topics: linear search, binary search, insertion sort, selection sort, bubble sort, shell sort, quick sort merge sort, bucket sort, m-way merge sort, polyphase merge sort, hashing techniques like separate chaining, closed chaining or open addressing, linear probing, quadratic probing, double hashing, rehashing and extendible hashing.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
The PPT would provide the Database Normalization is to restructure the logical data model of a database to:
Eliminate Redundancy
Organize Data Efficiently
Reduce the potential for Data Anomalies.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Here all UML diagrams for
ATM system, Library Managing System,
Online Banking System are posted refer it for UML/OOAD lab record (jntu B.tech 4-1 IT/CSE)
images not set properly dowload it and reduse the image size to view full images.
Students will be able to learn the various kinds of searching, sorting and hashing techniques to handle the data structures efficiently. This PPT contains the following topics: linear search, binary search, insertion sort, selection sort, bubble sort, shell sort, quick sort merge sort, bucket sort, m-way merge sort, polyphase merge sort, hashing techniques like separate chaining, closed chaining or open addressing, linear probing, quadratic probing, double hashing, rehashing and extendible hashing.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
The slides from my talk at the FOSDEM HPC, Big Data and Data Science devroom, which has general tips from various sources about putting your first machine learning model in production.
The video is available from the FOSDEM website: https://fosdem.org/2017/schedule/event/machine_learning_zoo/
This is used for brief talk about AI and its recent application in Machine Learning and Deep Learning field.
I'd like to ask your understanding about any missing references.
I appreciate you would comment about it.
I will immediately update the slides.
Slides of the course on big data by Clement Levallois from EMLYON Business School.
For business students. Check the online video connected with these slides.
-> Machine learning explained in simple terms to a business audience: what is a training set, a test set, and how does machine learning differ from statistics.
Slides for a Machine Learning Course in R,
includes an introduction to R and several ML methods for classification, regression, clustering and dimensionality reduction.
Microservices, containers, and machine learningPaco Nathan
http://www.oscon.com/open-source-2015/public/schedule/detail/41579
In this presentation, an open source developer community considers itself algorithmically. This shows how to surface data insights from the developer email forums for just about any Apache open source project. It leverages advanced techniques for natural language processing, machine learning, graph algorithms, time series analysis, etc. As an example, we use data from the Apache Spark email list archives to help understand its community better; however, the code can be applied to many other communities.
Exsto is an open source project that demonstrates Apache Spark workflow examples for SQL-based ETL (Spark SQL), machine learning (MLlib), and graph algorithms (GraphX). It surfaces insights about developer communities from their email forums. Natural language processing services in Python (based on NLTK, TextBlob, WordNet, etc.), gets containerized and used to crawl and parse email archives. These produce JSON data sets, then we run machine learning on a Spark cluster to find out insights such as:
* What are the trending topic summaries?
* Who are the leaders in the community for various topics?
* Who discusses most frequently with whom?
This talk shows how to use cloud-based notebooks for organizing and running the analytics and visualizations. It reviews the background for how and why the graph analytics and machine learning algorithms generalize patterns within the data — based on open source implementations for two advanced approaches, Word2Vec and TextRank The talk also illustrates best practices for leveraging functional programming for big data.
機器學習速遊 (Quick Tour of Machine Learning)
機器學習旨在讓電腦能由資料中累積的經驗來自我進步,近年來已廣泛應用於資料探勘、計算機視覺、自然語言處理、生物特徵識別、搜尋引擎、醫學診斷、檢測信用卡欺詐、證券市場分析、DNA序列測序、語音和手寫識別、戰略遊戲和機器人等領域。它已成為資料科學的基礎學科之一,為任何資料科學家必備的工具。
這門課程將由台大資訊工程系林軒田教授利用短短的六個小時,快速地帶大家探索機器學習的基石、介紹核心的模型及一些熱門的技法,希望幫助大家有效率而紮實地了解這個領域,以妥善地使用各式機器學習的工具。此課程適合所有希望開始運用資料的資料分析者,推薦給所有有志於資料分析領域的資料科學愛好者。
MLSEV. Models, Evaluations and Ensembles BigML, Inc
Introduction to Machine Learning. Supervised Learning (Part I): Models, Evaluations and Ensembles, by BigML.
MLSEV 2019: 1st edition of the Machine Learning School in Seville, Spain.
DutchMLSchool. Models, Evaluations, and EnsemblesBigML, Inc
DutchMLSchool. Introduction to Machine Learning, Models, Evaluations, and Ensembles (Supervised Learning I) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
2013 11-06 lsr-dublin_m_hausenblas_solr as recommendation enginelucenerevolution
This session will present a detailed tear-down and walk-through of a working soup-to-nuts recommendation engine that uses observations of multiple kinds of behavior to do combined recommendation and cross recommendation. The system is built using Mahout to do off-line analysis and Solr to provide real-time recommendations. The presentation will also include enough theory to provide useful working intuitions for those desiring to adapt this design.
The entire system including a data generator, off-line analysis scripts, Solr configurations and sample web pages will be made available on github for attendees to modify as they like.
Enhancing and Automating Decision Making with Machine Learning - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
VSSML17 L3. Clusters and Anomaly DetectionBigML, Inc
Valencian Summer School in Machine Learning 2017 - Day 1
Lecture 3: Clusters and Anomaly Detection. By Poul Petersen (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
The presentation is an introduction to AI (deep learning). The key to success with AI is “asking good questions.” The talk was given in "Seminar in Information Systems and Applications" at National Tsing Hua University in Taiwan. During this talk, we discussed what a good question is, how we use design thinking process to improve our question, and how can we “answer” the question by deep learning.
A fascinating View of the Artificial Intelligence Journey.
Ramón López de Mántaras, Ph.D.
Technical and Business Perspectives on the Current and Future Impact of Machine Learning - MLVLC
October 20, 2015
Real-world Stories and Long-term Risks and Opportunities.
Tom Dietterich, Ph.D.
Technical and Business Perspectives on the Current and Future Impact of Machine Learning - MLVLC
October 20, 2015
Valencian Summer School 2015
Day 2
Lecture 15
Machine Learning - Black Art
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 1
Lecture 9
Real World Machine Learning - Cooking Predictions
Andrés González (CleverTask)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 1
Lecture 7
A developers’ overview of the world of predictive APIs
Louis Dorard (PAPIs.io)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. State of the Art in Machine Learning
Poul Petersen
BigML
2. 2
What is ML?
“a field of study that gives computers the
ability to learn without being explicitly
programmed”
Professor Arthur Samuel, 1959
•What “ability to learn” do computers have?
•What does “explicitly programmed” mean?
4. 4
Ability to Learn
• Provide computer with examples of the relationship between
square footage X and price Y.
• The computer learns the equation of a line F() which fits the
examples.
• The computer can now predict the price of any home
knowing only the square footage: F( x ) = y
• This model is known as Linear Regression. There are other
types of models.
• You may have noticed there was some points that did not fit.
This is important!
5. 5
Learning Problems (fit)
Under-fitting Over-fitting
• Model does not fit well enough
• Does not capture the underlying trend
of the data
• Change algorithm or features
• Model fits too well does not “generalize”
• Captures the noise or outliers of the
data
• Change algorithm or filter outliers
6. 6
Learning Problems (missing)
• Missing values at training/prediction time
• Some algorithms can handle missing values, some no
• Missing data is sometimes important
• Replace missing values
• Predict missing values
8. 8
Not Explicitly Programmed
Control System
“Customers go on vacation… We need a program that
predicts if the light will come on when the control
system flips the switch.”
10. 10
Not Explicitly Programmed
Switch Light?
on TRUE
off FALSE
• The ML System reasoned the rules from the data and
created a Model
• Functionally the Model is the same as @iLoveRuby’s
explicit model
@iLoveML
question
answer
ML System Model
11. 11
Not Explicitly Programmed
• how long has the bulb has been in service
• reliability of brand
• rated power: higher power shorter life
• duty cycle
• room conditions: temperature, humidity
Goal is to predict if the bulb will come on
but the switch is not the important variable:
Even worse: None of these conditions is absolute.
17. 17
Supervised Learning
animal state … proximity action
tiger hungry … close run
elephant happy … far take picture
Classification
animal state … proximity min_kmh
tiger hungry … close 70
hippo angry … far 10
Regression
label
animal state … proximity action1 action2
tiger hungry … close run look untasty
elephant happy … far take picture call friends
Multi-Label Classification
18. 18
Unsupervised Learning
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
features
instances
Unlabeled Data
19. 19
Unsupervised Learning
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
Clustering
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
Anomaly Detection
similar
unusual
21. 21
Data Types
numeric
1 2 3
1, 2.0, 3, -5.4 categoricaltrue, yes, red, mammal categoricalcategorical
A B C
DATE-TIME2013-09-25 10:02
DATE-TIME
YEAR
MONTH
DAY-OF-MONTH
YYYY-MM-DD
DAY-OF-WEEK
HOUR
MINUTE
YYYY-MM-DD
YYYY-MM-DD
M-T-W-T-F-S-D
HH:MM:SS
HH:MM:SS
2013
September
25
Wednesday
10
02
text
Be not afraid of greatness:
some are born great, some
achieve greatness, and
some have greatness
thrust upon 'em.
text
“great”
“afraid”
“born”
“some”
appears 2 times
appears 1 time
appears 1 time
appears 2 times
22. 22
Text Analysis
Be not afraid of greatness:
some are born great, some
achieve greatness, and
some have greatness
thrust upon 'em.
23. 22
Text Analysis
Be not afraid of greatness:
some are born great, some
achieve greatness, and
some have greatness
thrust upon 'em.
great: appears 4 times
24. 23
Text Analysis
great afraid born achieve
4 1 1 1
… … … …
Be not afraid of greatness:
some are born great, some achieve
greatness, and some have greatness
thrust upon ‘em.
Model
The token “great”
does not occur
The token “afraid”
occurs more than once
27. 26
Why ML Now?
• Decreasing cost of data
• Abundant computing power, especially cloud
• Machine Learning APIs
• Abundance of APIs + internet to combine easily