You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
A pre conference workshop on Machine Learning was organized as a part of #doppa17, DevOps++ Global Summit 2017. The workshop was conducted by Dr. Vivek Vijay and Dr. Sandeep Yadav. All the copyrights are reserved with the author.
Machine Learning : why we should know and how it worksKevin Lee
The most popular buzz word nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes such as: self-driving vehicles; online recommendation on Netflix and Amazon; fraud detection in banks; image and video recognition; natural language processing; question answering machines (e.g., IBM Watson); and many more. This is leading many organizations to seek experts who can implement Machine Learning into their businesses.
Statistical programmers and statisticians in the pharmaceutical industry are in very interesting positions. We have very similar backgrounds as Machine Learning experts, such as programming, statistics, and data expertise, thus embodying the essential technical skill sets needed. This similarity leads many individuals to ask us about Machine Learning. If you are the leaders of biometric groups, you get asked more often.
The paper is intended for statistical programmers and statisticians who are interested in learning and applying Machine Learning to lead innovation in the pharmaceutical industry. The paper will start with the introduction of basic concepts of Machine Learning - hypothesis and cost function and gradient descent. Then, paper will introduce Supervised ML (e.g., Support Vector Machine, Decision Trees, Logistic Regression), Unsupervised ML (e.g., clustering) and the most powerful ML algorithm, Artificial Neural Network (ANN). The paper will also introduce some of popular SAS ® ML procedures and SAS Visual Data Mining and Machine Learning. Finally, the paper will discuss the current ML implementation, its future implementation and how programmers and statisticians could lead this exciting and disruptive technology in pharmaceutical industry.
Naive Bayes Classifier is a machine learning technique that is exceedingly useful to address several classification problems. It is often used as a baseline classifier to benchmark results. It is also used as a standalone classifier for tasks such as spam filtering where the naive assumption (conditional independence) made by the classifier seem reasonable. In this presentation we discuss the mathematical basis for the Naive Bayes and illustrate with examples
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we introduce classifiers ensembles.
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.
Tree models with Scikit-Learn: Great models with little assumptionsGilles Louppe
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.
This presentation covers Decision Tree as a supervised machine learning technique, talking about Information Gain method and Gini Index method with their related Algorithms.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
A pre conference workshop on Machine Learning was organized as a part of #doppa17, DevOps++ Global Summit 2017. The workshop was conducted by Dr. Vivek Vijay and Dr. Sandeep Yadav. All the copyrights are reserved with the author.
Machine Learning : why we should know and how it worksKevin Lee
The most popular buzz word nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes such as: self-driving vehicles; online recommendation on Netflix and Amazon; fraud detection in banks; image and video recognition; natural language processing; question answering machines (e.g., IBM Watson); and many more. This is leading many organizations to seek experts who can implement Machine Learning into their businesses.
Statistical programmers and statisticians in the pharmaceutical industry are in very interesting positions. We have very similar backgrounds as Machine Learning experts, such as programming, statistics, and data expertise, thus embodying the essential technical skill sets needed. This similarity leads many individuals to ask us about Machine Learning. If you are the leaders of biometric groups, you get asked more often.
The paper is intended for statistical programmers and statisticians who are interested in learning and applying Machine Learning to lead innovation in the pharmaceutical industry. The paper will start with the introduction of basic concepts of Machine Learning - hypothesis and cost function and gradient descent. Then, paper will introduce Supervised ML (e.g., Support Vector Machine, Decision Trees, Logistic Regression), Unsupervised ML (e.g., clustering) and the most powerful ML algorithm, Artificial Neural Network (ANN). The paper will also introduce some of popular SAS ® ML procedures and SAS Visual Data Mining and Machine Learning. Finally, the paper will discuss the current ML implementation, its future implementation and how programmers and statisticians could lead this exciting and disruptive technology in pharmaceutical industry.
Naive Bayes Classifier is a machine learning technique that is exceedingly useful to address several classification problems. It is often used as a baseline classifier to benchmark results. It is also used as a standalone classifier for tasks such as spam filtering where the naive assumption (conditional independence) made by the classifier seem reasonable. In this presentation we discuss the mathematical basis for the Naive Bayes and illustrate with examples
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we introduce classifiers ensembles.
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.
Tree models with Scikit-Learn: Great models with little assumptionsGilles Louppe
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.
This presentation covers Decision Tree as a supervised machine learning technique, talking about Information Gain method and Gini Index method with their related Algorithms.
Robot Motor Skill Coordination with EM-based Reinforcement LearningPetar Kormushev
A Barrett WAM robot learns to flip pancakes by reinforcement learning.
The motion is encoded in a mixture of basis force fields through an extension of Dynamic Movement Primitives (DMP) that represents the synergies across the different variables through stiffness matrices. An Inverse Dynamics controller with variable stiffness is used for reproduction.
The skill is first demonstrated via kinesthetic teaching, and then refined by Policy learning by Weighting Exploration with the Returns (PoWER) algorithm. After 50 trials, the robot learns that the first part of the task requires a stiff behavior to throw the pancake in the air, while the second part requires the hand to be compliant in order to catch the pancake without having it bounced off the pan.
5.DECISION MAKING PROCESS :-
Recognizing & defining the situation
Identifying the alternatives
Evaluating the alternatives
Apply the model
Selecting the best alternatives
Conduct a sensitivity of the solution
Implementing the chosen alternatives
Following up & evaluating the result
6.TYPE OF DECISION MAKING ENVIRONMENT
Decision making under certainty
Decision making under uncertainty
Decision making under risk
23.DECISION TREE :
Instances describable by attribute-value pairs
e.g Humidity: High, Normal
Target function is discrete valued
e.g Play tennis; Yes, No
Disjunctive hypothesis may be required
e.g Outlook=Sunny Wind=Weak
Possibly noisy training data
Missing attribute values
Application Examples:
Medical diagnosis
Credit risk analysis
Object classification for robot manipulator (Tan 1993)
25.Bayesian analysis
26.Utility theory :
Step for determine the utility for money :
Develop a payoff table using monetary values
Identify the best and worst payoff value
For every other monetary value in the original payoff table
Convert the payoff table from monetary value to calculate utility value.
Apply the expected utility criterion to the utility table and select the decision alternative with the best expected utility.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
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)
Course: Intro to Computer Science (Malmö Högskola):
knowledge representation and abstraction, decision making, generalization, data acquistion (abstraction), machine learning, similarity
another version of abstraction