machine learning presentation pdf

© 2008-2020 ResearchGate GmbH. Machine-learning identifies hidden patterns in knowledge-intensive processes and learns from the data without being explicitly programmed Robotics process automation helps run repetitive, rule-based, and user interface– focused tasks and bridges temporary gaps Rule engines Machine-learning Robotic process automation 3 0 obj Number of kernel evaluations: 15736 (68.637% cached), Correctly Classified Instances 328 70.9957 %, Incorrectly Classified Instances 134 29.0043 %, Kappa statistic 0.3319, Mean absolute error 0.29, Root mean squared 0.5386, Relative absolute error 64.028 %, Coverage of cases (0.95 level) 70.9957 %, 0.825 0.506 0.755 0.825 0.788 0.335 0.659 0.737 0, 0.494 0.175 0.598 0.494 0.541 0.335 0.659 0.471 1, Here, we can see that the said SVM performs better than the Naïve Bayes classifier for, class 0, predicting 82.5% of the classes correctly, whereas it performs slightly worse than Naïve, Bayes for class 1 with 49.4%. Machine Learning 10-601, Spring 2015 Carnegie Mellon University Tom Mitchell and Maria-Florina Balcan : Home. Using Bayes' theorem, the conditional probability can be decomposed as: independence assumptions, we can say that. Artificial Intelligence: A Modern Approach. Fractal theory is the study of irregularity which occurs in natural objects. In this project, we were asked to experiment with a real world dataset, and to explore how However, there is still a vast majority, of the dataset, i.e. All rights reserved. Unlike other review papers such as [9]–[11], the presentation aims at highlighting conditions under which the use of machine learning is justified in engineering problems, as well as specific classes of learning algorithms that are experience using a common data-mining and machine learning library, Weka, and were expected Marc Francke (UvA) Big data, machine learning, and econometrics 24/48. Higher Performance Machine Learning Models Placement model (trained via RL) gets graph as input + set of devices, outputs device placement for each graph node Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini, Hieu Pham, Quoc Le, Mohammad Norouzi, Samy Bengio, Benoit Steiner, Yuefeng Zhou, 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. [5] We, (Sequential Minimal Optimization) algorithm to train support vector machines[7,8,9]. Since it’s a binary dataset with the class label being either the person has CHD or s/he. You're not alone. Download Machine Learning Paper Presentation doc. Please note that Youtube takes some time to process videos before they become available. In practice, if the data scientist can, manually remove irrelevant features from the input data, this is likely to improve the, accuracy of the learned function. Find materials for this course in the pages linked along the left. butest. MIT Press, 2012. Download Machine Learning Paper Presentation pdf. Access scientific knowledge from anywhere. Don't show me this again. Is SIEM really Dead ? ResearchGate has not been able to resolve any citations for this publication. as described in Rousseauw et al, 1983, South African Medical Journal, and has the following, In the dataset, there are 462 example vectors. Advances in 2nd Edition. Machine Learning is applied to nd patterns in the communication among the agents. There is usually a method to the madness, and in this chapter I’ll show you some of the common patterns used in creating a professionally designed system. Kevin Murphy. Machine learning prediction of stock markets Nikola Milosevic. Curious about why the data was behaving the way it was, we did use other classifiers on. Pearson Education Limited, 2013. dimensions for better predictions, and with the given feature vectors, vectors missing from it. Our procedure is justified via the Johnson-Lindenstrauss lemma-the pairwise distances between data points are preserved through random projections. 28.4% which can’t be correctly classified. endobj This was used on the aforementioned dataset, which led to the following output: === Classifier model (full training set) ===, Correctly Classified Instances 331 71.645 %, Incorrectly Classified Instances 131 28.355 %, Kappa statistic 0.3855, Mean absolute error 0.3238, Relative absolute error 71.4816 %, Coverage of cases (0.95 level) 92.4242 %, 0.762 0.369 0.796 0.762 0.778 0.386 0.749 0.843 0, 0.631 0.238 0.584 0.631 0.607 0.386 0.749 0.580 1, with the True Positive classification rate being 71.6 percent on an average, i.e. Diplomsko Delo. 0.6795 1 0.516adiposity+0.46 age+0.401obesity+0.334ldl+0.324sbp... 0.5465 2 0.543alcohol+0.459tobacco-0.392obesity-0.364ldl-0.282typea... 0.4269 3 -0.792typea-0.459alcohol+0.338famhist+0.135age+0.125sbp... 0.322 4 -0.833famhist-0.305obesity-0.258alcohol-0.21typea-0.196sbp... 0.2291 5 0.624tobacco-0.419alcohol+0.321typea+0.305famhist-0.283obesity... 0.1446 6 0.781sbp-0.379alcohol+0.332typea-0.215ldl-0.174obesity... 0.0706 7 0.788ldl-0.333obesity+0.277alcohol+0.268sbp-0.196adiposity... 0.0194 8 0.691age-0.489tobacco-0.339obesity-0.235sbp+0.187famhist... been deemed unworthy by the PCA implementation in WEKA, which made little sense to us as, age is highly correlated to most diseases. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book. These pattern are used to provide a human user of ProPlanT with useful information, enabling him to optimize the system. Pattern Recognition and Machine Learning. Datasets:Coronary Heart Disease Dataset." L��P��ȲYs����L���rL�M㹁]�����II�:��h ez����ZE55�.6�;�s�_�lDy�4C$ ���zA:`ƃ�t�Hm����i26h}0�#��1�g F.���?�Y]���V4�j�7v�� {LKc�SF������C��P��Y�E��xAg��?6�h-s����ȇ�m��'0�JV]h�I|�f�|Š�)�Zr��O�{�,�֥���������&h�UU�큙N�Ș��iA��dl�sX��m�V���,a��q�Q��^������C#�A(. The concept of machine learning is something born out of this environment. This result is surprising, as we expected SVM to, perform better than the Naïve Bayes Classifier for independent non-redundant feature vectors as, SVM projects low-dimensional sub-space to a higher dimensional subspace where the features, are linearly separable. The corresponding classifier, a. WEKA[5,9]. In this, case, the algorithm must not attempt to infer the function that exactl, data. In this page you will find a set of useful articles, videos and blog posts from independent experts around the world that will gently introduce you to the basic concepts and techniques of Machine Learning. The machine learning is a sort of artificial intelligence that enables the computers to learn without being explicitly programmed. Machine Learning: A Probabilistic Perspective. Expert Systems have been used in the field. Being too careful in fitting the data can cause overfitting, after which the m, will answer perfectly for all training examples but will have a very high error for, Only after considering all these factors can we pick a supervised learning algorithm that, works for the dataset we are working on. We used. "Weka 3: Data Mining Software in Java." transformation and learning methods. data is one of the most straightforward ways to quickly gain insights and make predictions. 9. It, results in two doctors, one of them virtual, instead of one doctor diagnosing every case which has. this classifier can, correctly classify 71.6 percent of all the examples it sees. Data Mining: Practical Machine Learning Tools and Techniques, Data Mining: Practical Machine Learning Tools and Techniques (Third Edition), Random Projection Estimation of Discrete-Choice Models with Large Choice Sets, Application of Machine Learning Methods in a Multi-Agent System. S.l. certain machine learning algorithms. of medical science to assist the doctors in making certain diagnoses, and this can help save lives. Machine Learning. / Many variables will influence the prediction (classification). “I'm going to talk about I2E and Machine Learning, and I'll start by talking about AI in general, NLP, and machine learning. S.l. Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. In layman’s terms, supervised learning can be termed as the process of concept learning, where a brain is exposed to, a set of inputs and result vectors and the brain learns the concept that relates said inputs to, learning enthusiast, for example Neural Networks, Decision Trees, Support V, Random Forest, Naïve Bayes Classifier, Bayes Net, Majority Classifier[4,7,8,9] etc., and they, each have their own merits and demerits. New. Automated Machine Learning (AutoML) •Goal: let non-experts build prediction models, and make model fitting less tedious •Let the machine build the best possible “pipeline” of pre-processing, feature (=predictor) construction and selection, model selection, and parameter optimization •Using TPOT, an open source python framework Some necessary and sufficient conditions for a product IFS to be just touching are discussed. Problems and Issues in Supervised learning: Before we get started, we must know about how to pick a good machine learning. Previous material . on a dataset of my choice, herein lies my final report. We get the. Learning: Data Mining, Inference, and Prediction: With 200 Full-color Illustrations. Schö lkopf, Bernhard, Christopher J. C. Burges, and Alexander J. Smola. Are you new to Machine Learning? Machine Learning for Dummies Venkata Reddy Konasani. / Large scale of data. Topic 2 - Intro to Data Science Machine Learning.ppt - Free download as Powerpoint Presentation (.ppt), PDF File (.pdf), Text File (.txt) or view presentation slides online. Although machine learning applications vary, its Project. In the future, if similar studies are conducted to generate the, dataset used in this report, more feature vectors need to be calculated so that the classifiers can. Artificial Intelligence: A Modern Approach. Machine Learning: An Overview: The slides presentintroduction to machine learningalong with some of the following: 1. [4,7,9] for this purpose and came up with the following results. hi These techniques can be used . In Supervised learning, we have a After performing the required tasks. Machine Learning - Introduction •It is a scientific discipline concerned with the design and … 2 0 obj Moreover, if the, doctor makes a slip, i.e. Naivni Bayesov Klasifikator: We were expected to gain Aujas. We introduce random projection, an important dimension-reduction tool from machine learning, for the estimation of aggregate discrete-choice models with high-dimensional choice sets. In addition, there are many algorithms for feature, selection that seek to identify the relevant features and discard the irreleva, The programmer should know that there is a possibility that the output values may, constitute of an inherent noise which is the result of human or sensor errors. Furthermore, strong convergence results are established in a re exive Banach space. Machine learning emphases on the development of computer programs that can teach themselves to change and grow when disclosed to new or unseen data. Algorithmic modeling Machine learning is alchemy AI researchers allege that machine learning is alchemy-‘Rahimi [working for Google] charged that machine learning algorithms, in which computers learn … For example, if we were working with a dataset, consisting of heterogeneous data, then decision trees would fare better than other algorithms. combine this model with a decision rule, and one of the common rules is to pick which, hypothesis is the most probable. Join ResearchGate to find the people and research you need to help your work. Computers can analyze digital data to find patterns and laws in ways that is too complex for a human to do. There is no single algorithm that works for all cases, as, which is a sample of males in a heart-disease high risk region of South Africa, and attempt to. Machine learning is most appropriate when: / There are lots of variables. <> x��\Yo�F~���/���#0�ڬv�A�!�š4��H����_�]�7ɣp� Q��]_}u�\�n��m�w�����]��bO~����?.�������Xfݱ*/�o:8��"�ͫW�ͻ����3�:�O�z�!��i���~ ��ٛ��˿��u�����3�]��nD��ĉK��\?��~�5&w-{&�ãD�t~���l� ��{Ϟ����ӯ���x!�B�.��%� IS~���(�&�5�Ҁ]��#~J��x��^7?�0 i��Sʧ�3�� ��������O ��o��G�{�{4�# ��0�9��f?�1�;��C��z_=dwEJ�Ud�e�B��œ��4dot���l?mwަ)��`�n� ��o�nl2���>�����\�s�����������{8�xs�'�>��ß�q�n�/����=�TT��5�3s}tG��N�9{��G�(e0��?c�O�?�#r�qLӱ�|bi@xE[l1��u��H��6�d_���l��������5�va�y�N^o]�/��p`��N~��0���A7�!�-\G���Y�NL�tn���=Xp�#����#OA����&�p/���Y�=��1��ܻ6W �@:ۘM=��mwɦR`@l J*��8�6�=���]W�[��SW��^e%� �2P�v 8-���)c��/�� �[՛�/�ý :�m;�gT5��oƘ���o>owL�p|��5���0�+u����0�B�WC��tr�K�Hbv� �g6���д,�ֵȶbjL*l�p�(JĘ�-�Yir��`q���WXW!M�G�$gcs�0o�hzd?�hs˄'�� bw��r8w@��O�Du�T����,�-IKq,�H�Mi�l�7\��\��/\���P9�F�E��G�P����s������ga&�m�gufE�"_�UġQC�����A/����RIb$����:`rB�M��bnrĩ��" contain highly correlated values, then it’s useless to use distance based methods because of numerical instabilit, this case, some sort of Regularization can be employed to the data to prevent this, If there is some dependence between the feature vectors, then algorithms that, monitor complex interactions like Neural Networks and Decision Trees fare better, A learning algorithm is biased for a particular input x if, when trained on each of. The Elements of Statistical 1 Machine learning optimization of peptides for presentation by class II 2 MHCs 3 4 Zheng Dai sátá , Brooke D. Huisman uá , Haoyang Zeng 1,2, Brandon Carter 1,2, Siddhartha Jain 1,2, 5 Michael E. Birnbaum 3 *, David K. Gifford 1,2,3 *, 6 7 1 Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA York: Springer, 2001. Different types of learning (supervised, unsupervised, reinforcement) 2. If you take the latex, be sure to also take the accomanying style files, postscript figures, etc. Initially, high-dimensional data are projected into a lower-dimensional Euclidean space using random projections. Artificial Intelligence: A Modern Approach. The prediction error of, a learned classifier can be related to the sum of bias and variance of the learning, algorithm, and neither can be high as they will make the prediction error to be high. form a better idea of the problem at hand. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. requires the model to generalize from the training set in a reasonable way. with 100 trees, and the only classifier that got close was the J48 with true positive rate of 70.7%. Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one. A framework of tools has been developed, that allows the application of dierent. algorithms, sparse dictionary learning, etc. [9], The Elements of Statistical Learning: Data Mining, Inference, and Prediction: With 200 Full-color IllustrationsWeka 3: Data Mining Software in Java Weka 3, Hastie, Trevor, Robert Tibshirani, and J. H. Friedman. Dimensions of a learning system (different types of feedback, representation, use of knowledge) 3. We were expected to gain, experience using a common data-mining and machine learning library, Weka, and were expected, to submit a report about the dataset and the algorithms used. Through combined results of PCA and SAE, we conclude that all the features, are relevant for our purposes. Supervised learning, or classification is the machine, learning task of inferring a function from a labeled data [2]. People . This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Artificial Intelligence Abbas Hashmi. All content in this area was uploaded by Manish Bhatt on May 18, 2016, In this project, we were asked to experiment with a real world dataset, and to ex, machine learning algorithms can be used to find the patterns in data. The basic idea of machine learning is that a computer can automatically learn from experience (Mitchell, 1997). The RMS error for SVM was comparatively higher compared to Naïve, Bayes by .10 and the kappa statistic of Naïve Bayes was lower than SVM by .05, which shows. A method to generate an open set which satisfies the open set condition for a totally disconnected IFS is given. that Naïve Bayes is the better classifier. the age of the patient was the most significant factor for, classification purposes, and factors 7 and 8, obesity and alcohol consumption were the least, significant factors. and psychologists study learning in animals and humans. Advantages and disadvantages of Machine Learning Course - Advantages and disadvantages of Machine Learning course attend free demo classes on Machine Learning Online Training in India and know why one needs to choose machine learning. OR Can it evolve into a Platform ? to submit a report about the dataset and the algorithms used. throw various intelligently-picked algorithms at the data, and see what sticks. Pearson Education Limited, 2013. Machine Learning presentation. If, the input space of the dataset we were working on had 1000 dimensions, then it’s better to first. The algorithms that, employ distance metrics are very sensitive to this, and hence if the data is, heterogeneous, these methods should be the afterthought. <> stream lkopf, Bernhard, Christopher J. C. Burges, and Alexander J. Smola. While Machine Learning can be incredibly powerful when used in the right ways and in the right places ML offers huge advantages to the sales and … With machine learning being covered so … In this book we fo-cus on learning in machines. multinomial choice model; the estimation procedure is semiparametric and does not require explicit distributional assumptions to be made regarding the random utility errors. Interpretable AI I Interpretable AI is a machine learning software company that builds technologies simultaneously delivering interpretability and state-of-the-art performance. of PCA and SAE, no other pre-processing was done on the data. If the problem has an input space that has a large number of dimensions, and the, problem only depends on a subspace of the input space with small dimensions, the, machine learning algorithm can be confused by the huge number of dimensions and, hence the variance of the algorithm can be high. This is one of over 2,200 courses on OCW. After performing the required tasks It is a conditional probabilit, given a problem instance to be classified, represented by a vector, some n features (independent variables), it assigns to this instance probabilities, for each of K possible outcomes or classes. We can use machine learning algorithms to determine the rules from the data. Machine learning What we were attempting to generalize is a subspace of the actual input, space, where the other dimensions are not known, and hence none of the classifiers were able to, do better than 71.6% (Naïve Bayes).

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