The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Half day session on Machine learning and its applications. It introduces Artificial Intelligence, move on Machine Learning, applications, algorithms, types, using Cloud for ML, Deep Learning and some resources to start with
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Half day session on Machine learning and its applications. It introduces Artificial Intelligence, move on Machine Learning, applications, algorithms, types, using Cloud for ML, Deep Learning and some resources to start with
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
This presentation was prepared as part of the curriculum studies for CSCI-659 Topics in Artificial Intelligence Course - Machine Learning in Computational Linguistics.
It was prepared under guidance of Prof. Sandra Kubler.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
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.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset.
Below topics are explained in this Support Vector Machine presentation:
1. What is Machine Learning?
2. Why support vector machine?
3. What is support vector machine?
4. Understanding support vector machine
5. Advantages of support vector machine
6. Use case in Python
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Tutorial on Deep learning and ApplicationsNhatHai Phan
In this presentation, I would like to review basis techniques, models, and applications in deep learning. Hope you find the slides are interesting. Further information about my research can be found at "https://sites.google.com/site/ihaiphan/."
NhatHai Phan
CIS Department,
University of Oregon, Eugene, OR
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
This presentation was prepared as part of the curriculum studies for CSCI-659 Topics in Artificial Intelligence Course - Machine Learning in Computational Linguistics.
It was prepared under guidance of Prof. Sandra Kubler.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
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.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset.
Below topics are explained in this Support Vector Machine presentation:
1. What is Machine Learning?
2. Why support vector machine?
3. What is support vector machine?
4. Understanding support vector machine
5. Advantages of support vector machine
6. Use case in Python
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Tutorial on Deep learning and ApplicationsNhatHai Phan
In this presentation, I would like to review basis techniques, models, and applications in deep learning. Hope you find the slides are interesting. Further information about my research can be found at "https://sites.google.com/site/ihaiphan/."
NhatHai Phan
CIS Department,
University of Oregon, Eugene, OR
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Data By The People, For The People
Daniel Tunkelang
Director, Data Science at LinkedIn
Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful.
Bio:
Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
Introduction to Mahout and Machine LearningVarad Meru
This presentation gives an introduction to Apache Mahout and Machine Learning. It presents some of the important Machine Learning algorithms implemented in Mahout. Machine Learning is a vast subject; this presentation is only a introductory guide to Mahout and does not go into lower-level implementation details.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
Myths and Mathemagical Superpowers of Data ScientistsDavid Pittman
Some people think data scientists are mythical beings, like unicorns, or they are some sort of nouveau fad that will quickly fade. Not true, says IBM big data evangelist James Kobielus. In this engaging presentation, with artwork created by Angela Tuminello, Kobielus debunks 10 myths about data scientists and their role in analytics and big data. You might also want to read the full blog by Kobielus that spawned this presentation: "Data Scientists: Myths and Mathemagical Superpowers" - http://ibm.co/PqF7Jn
For more information, visit http://www.ibmbigdatahub.com
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Marina Santini
In this lecture, we talk about two different discriminative machine learning methods: decision trees and k-nearest neighbors. Decision trees are hierarchical structures.k-nearest neighbors are based on two principles: recollection and resemblance.
Presentation of research paper 'Study of some data mining classification techniques'(International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056 p-ISSN: 2395-0072 Volume: 04 Issue: 04 | Apr -2017) for a academic purpose during post graduation. in this study i describe some data mining classification techniques such as ANN, SVM,decision tree with example.
MS CS - Selecting Machine Learning AlgorithmKaniska Mandal
ML Algorithms usually solve an optimization problem such that we need to find parameters for a given model that minimizes
— Loss function (prediction error)
— Model simplicity (regularization)
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.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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.
An Introduction to Supervised Machine Learning and Pattern Classification: The Big Picture
1. Practical Data Science
An Introduction to Supervised Machine Learning
and Pattern Classification: The Big Picture
Michigan State University
NextGen Bioinformatics Seminars - 2015
Sebastian Raschka
Feb. 11, 2015
2. A Little Bit About Myself ...
Developing software & methods for
- Protein ligand docking
- Large scale drug/inhibitor discovery
PhD candidate in Dr. L. Kuhn’s Lab:
and some other machine learning side-projects …
3. What is Machine Learning?
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
"Field of study that gives computers the
ability to learn without being explicitly
programmed.”
(Arthur Samuel, 1959)
By Phillip Taylor [CC BY 2.0]
5. Examples of Machine Learning
http://googleresearch.blogspot.com/2014/11/a-picture-is-worth-thousand-coherent.html
By Steve Jurvetson [CC BY 2.0]
Self-driving cars
Photo search
and many, many
more ...
Recommendation systems
http://commons.wikimedia.org/wiki/File:Netflix_logo.svg [public domain]
6. How many of you have used
machine learning before?
8. Learning
• Labeled data
• Direct feedback
• Predict outcome/future
• Decision process
• Reward system
• Learn series of actions
• No labels
• No feedback
• “Find hidden structure”
Unsupervised
Supervised
Reinforcement
12. Feature Extraction
Feature Selection
Dimensionality Reduction
Feature Scaling
Raw Data Collection
Pre-Processing
Sampling
Test Dataset
Training Dataset
Learning Algorithm
Training
Post-Processing
Cross Validation
Final Classification/
Regression Model
New DataPre-Processing
Refinement
Prediction
Split
Supervised
Learning
Sebastian Raschka 2014
Missing Data
Performance Metrics
Model Selection
Hyperparameter
Optimization
This work is licensed under a Creative Commons Attribution 4.0 International License.
Final Model
Evaluation
13. Feature Extraction
Feature Selection
Dimensionality Reduction
Feature Scaling
Raw Data Collection
Pre-Processing
Sampling
Test Dataset
Training Dataset
Learning Algorithm
Training
Post-Processing
Cross Validation
Final Classification/
Regression Model
New DataPre-Processing
Refinement
Prediction
Split
Supervised
Learning
Sebastian Raschka 2014
Missing Data
Performance Metrics
Model Selection
Hyperparameter
Optimization
This work is licensed under a Creative Commons Attribution 4.0 International License.
Final Model
Evaluation
14. A Few Common Classifiers
Decision Tree
Perceptron Naive Bayes
Ensemble Methods: Random Forest, Bagging, AdaBoost
Support Vector Machine
K-Nearest Neighbor
Logistic Regression
Artificial Neural Network / Deep Learning
15. Discriminative Algorithms
Generative Algorithms
• Models a more general problem: how the data was generated.
• I.e., the distribution of the class; joint probability distribution p(x,y).
• Naive Bayes, Bayesian Belief Network classifier, Restricted
Boltzmann Machine …
• Map x → y directly.
• E.g., distinguish between people speaking different languages
without learning the languages.
• Logistic Regression, SVM, Neural Networks …
16. Examples of Discriminative Classifiers:
Perceptron
xi1
xi2
w1
w2
Σ yi
y = wTx = w0 + w1x1 + w2x2
1
F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.
x1
x2
y ∈ {-1,1}
w0
wj = weight
xi = training sample
yi = desired output
yi = actual output
t = iteration step
η = learning rate
θ = threshold (here 0)
update rule:
wj(t+1) = wj(t) + η(yi - yi)xi
1 if wTxi ≥ θ
-1 otherwise
^
^
^
^
yi
^
until
t+1 = max iter
or error = 0
17. Discriminative Classifiers:
Perceptron
F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.
- Binary classifier (one vs all, OVA)
- Convergence problems (set n iterations)
- Modification: stochastic gradient descent
- “Modern” perceptron: Support Vector Machine (maximize margin)
- Multilayer perceptron (MLP)
xi1
xi2
w1
w2
Σ yi
1
y ∈ {-1,1}
w0
^
x1
x2
18. Generative Classifiers:
Naive Bayes
Bayes Theorem: P(ωj | xi) =
P(xi | ωj) P(ωj)
P(xi)
Posterior probability =
Likelihood x Prior probability
Evidence
Iris example: P(“Setosa"| xi), xi = [4.5 cm, 7.4 cm]
21. Generative Classifiers:
Naive Bayes
- Naive conditional independence assumption typically
violated
- Works well for small datasets
- Multinomial model still quite popular for text classification
(e.g., spam filter)
24. Decision Tree
Entropy =
depth = 4
petal length <= 2.45?
petal length <= 4.75?
Setosa
Virginica Versicolor
Yes No
e.g., 2 (- 0.5 log2(0.5)) = 1
∑−pi logk pi
i
Information Gain =
entropy(parent) – [avg entropy(children)]
NoYes
depth = 2
25. "No Free Lunch" :(
Roughly speaking:
“No one model works best for all possible situations.”
Our model is a simplification of reality
Simplification is based on assumptions (model bias)
Assumptions fail in certain situations
D. H. Wolpert. The supervised learning no-free-lunch theorems. In Soft Computing and Industry, pages 25–42. Springer, 2002.
26. Which Algorithm?
• What is the size and dimensionality of my training set?
• Is the data linearly separable?
• How much do I care about computational efficiency?
- Model building vs. real-time prediction time
- Eager vs. lazy learning / on-line vs. batch learning
- prediction performance vs. speed
• Do I care about interpretability or should it "just work well?"
• ...
27. Feature Extraction
Feature Selection
Dimensionality Reduction
Feature Scaling
Raw Data Collection
Pre-Processing
Sampling
Test Dataset
Training Dataset
Learning Algorithm
Training
Post-Processing
Cross Validation
Final Classification/
Regression Model
New DataPre-Processing
Refinement
Prediction
Split
Supervised
Learning
Sebastian Raschka 2014
Missing Data
Performance Metrics
Model Selection
Hyperparameter
Optimization
This work is licensed under a Creative Commons Attribution 4.0 International License.
Final Model
Evaluation
28. Missing Values:
- Remove features (columns)
- Remove samples (rows)
- Imputation (mean, nearest neighbor, …)
Sampling:
- Random split into training and validation sets
- Typically 60/40, 70/30, 80/20
- Don’t use validation set until the very end! (overfitting)
Feature Scaling:
e.g., standardization:
- Faster convergence (gradient descent)
- Distances on same scale (k-NN with Euclidean distance)
- Mean centering for free
- Normal distributed data
- Numerical stability by avoiding small weights
z =
xik - μk
σk
(use same parameters for the test/new data!)
29. Categorical Variables
color size prize class
label
0 green M 10.1 class1
1 red L 13.5 class2
2 blue XL 15.3 class1
ordinalnominal
green → (1,0,0)
red → (0,1,0)
blue → (0,0,1)
class
label
color=blue color=green color=red prize size
0 0 0 1 0 10.1 1
1 1 0 0 1 13.5 2
2 0 1 0 0 15.3 3
M → 1
L → 2
XL → 3
30. Feature Extraction
Feature Selection
Dimensionality Reduction
Feature Scaling
Raw Data Collection
Pre-Processing
Sampling
Test Dataset
Training Dataset
Learning Algorithm
Training
Post-Processing
Cross Validation
Final Classification/
Regression Model
New DataPre-Processing
Refinement
Prediction
Split
Supervised
Learning
Sebastian Raschka 2014
Missing Data
Performance Metrics
Model Selection
Hyperparameter
Optimization
This work is licensed under a Creative Commons Attribution 4.0 International License.
Final Model
Evaluation
36. Test set
Training dataset Test dataset
Complete dataset
Test set
Test set
Test set
1st iteration calc. error
calc. error
calc. error
calc. error
calculate
avg. error
k-fold cross-validation (k=4):
2nd iteration
3rd iteration
4th iteration
fold 1 fold 2 fold 3 fold 4
Model Selection
38. Feature Selection
- Domain knowledge
- Variance threshold
- Exhaustive search
- Decision trees
- …
IMPORTANT!
(Noise, overfitting, curse of dimensionality, efficiency)
X = [x1, x2, x3, x4]start:
stop:
(if d = k)
X = [x1, x3, x4]
X = [x1, x3]
Simplest example:
Greedy Backward Selection
39. Dimensionality Reduction
• Transformation onto a new feature subspace
• e.g., Principal Component Analysis (PCA)
• Find directions of maximum variance
• Retain most of the information
48. Feature Extraction
Feature Selection
Dimensionality Reduction
Feature Scaling
Raw Data Collection
Pre-Processing
Sampling
Test Dataset
Training Dataset
Learning Algorithm
Training
Post-Processing
Cross Validation
Final Classification/
Regression Model
New DataPre-Processing
Refinement
Prediction
Split
Supervised
Learning
Sebastian Raschka 2014
Missing Data
Performance Metrics
Model Selection
Hyperparameter
Optimization
This work is licensed under a Creative Commons Attribution 4.0 International License.
Final Model
Evaluation
51. Inspiring Literature
P. N. Klein. Coding the Matrix: Linear
Algebra Through Computer Science
Applications. Newtonian Press, 2013.
R. Schutt and C. O’Neil. Doing Data
Science: Straight Talk from the Frontline.
O’Reilly Media, Inc., 2013.
S. Gutierrez. Data Scientists at Work.
Apress, 2014.
R. O. Duda, P. E. Hart, and D. G. Stork.
Pattern classification. 2nd. Edition. New
York, 2001.