Machine Learning: an Introduction and cases
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The document is a disclaimer for an educational presentation on machine learning that will be given by Hendri Karisma. It states that the presentation is intended for educational purposes only and does not replace independent professional judgment. It also notes that any opinions or information presented are those of the individual participants and may not reflect the views of the company, and that the company does not endorse or approve the content.
![Fraud Detection
Payment Fraud (phishing,
account take-over, carding)
System abuse (promo, content,
account, logistic and payment
methods especially COD)
Fraud not only result in financial
losses but also produce some
reputational risk.
Some security measures has
been taken by bank or another
multinational finance service.
[E. Duman et al, 2013]](https://image.slidesharecdn.com/aimlshare-191023044428/85/Machine-Learning-an-Introduction-and-cases-56-320.jpg)
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Machine Learning: an Introduction and cases
- 1. Disclaimer Presentations are intended for educational purposes only and do not replace independent professional judgment. Statements of fact and o p i n i o n s e x p r e s s e d a r e t h o s e o f t h e participants individually and don’t necessarily reflect those of blibli.com. Blibli.com does not endorse or approve, and assumes no responsibility for, the content, accuracy or completeness of the information presented.
- 2. Machine Learning: an Introduction and cases Hendri Karisma [email protected]
- 3. Hendri Karisma • Sr. Research and Development Engineer at blibli.com (PT. Global Digital Niaga) • R&D Team in AI Squad • Working for Fraud Detection System, Customer Group for abuser detection, dynamic recommendation system project and Customer Segmentation. • https://about.me/hendriKarisma
- 4. Materials • Definition and background • Methods • Problems and Solutions • Technologies • Cases
- 5. The Definition of Informatics “Automation of Information” – Prof. Dr. Ing. Iping Supriana
- 6. Artificial Intelligence S. Rusel and P. Norvig, Artificial Intelligence in Modern Approach
- 7. Problem Solving agent Searching for solution Knowledge Base and Planning Reasoning Learning
- 9. Data ???
- 10. Machine Learning Definition “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” – Prof. Tom Mitchel
- 11. Problem and Solutions Analytical (Exact) Example : — analytics solution : — Numerical solution — Error = | 7.25 – 22/3| = |7.25-7.33|=0.08333 Numerical (Aprox) — Is numerical methods just about ML method that we know in the book? — Newton raphson, Gauss Elimination, Gauss-Jordan, Jacobi method, Gauss- Seidel, Lagrange, Newton Gregory, Richardson Interpolation, etc.
- 12. How it works Count the error ( y - y') Then minimize the error or Maximize the likelihood
- 13. Machine Learning Function Information Theory (Decission Tree : ID-Tree, C4.5, etc) Probability (Bayessian : Naive Bayes, Belief Network, etc) Graphical Model (Belief network, HMM, CRF, Neural Network, etc) Numerical Method / Regression (Stochastic Gradient Descent: Linear Regression, Multiple Linear Regression, Neural Network, Stochastic Gradient Ascent : E-M Algorithm)
- 14. Machiner Learning Taxonomy Supervised Unsupervised Reinforcement Learning Semi-Supervised Deep Learning
- 15. Machine Learning Taxonomy #2
- 16. Methods
- 18. Regression
- 19. Deep Learning
- 20. Deep Learning
- 21. The four layer of Datamining
- 22. Problems
- 23. Complexity #1
- 24. Complexity #2
- 25. Solutions
- 26. Solutions #1
- 27. Solutions #2 In-memory data fabric: provides low-latency access and processing of large quantities of data by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system
- 28. Solutions #3 Cluster machine GPU Machine (OpenCL and nVidia CUDA)
- 29. Technologies
- 30. Tools on python Numpy Scipy Pandas Scikit-learn Matplotlib Seaborn Tensorflow *pydata.org *anaconda Other Tech (to support ML) : – Apache Kafka – Apache Spark – Db: mongo,postgre – elasticsearch – CUDA/OpenCL
- 31. Tools on java virtual machine (jvm) Weka Deeplearin4j (working with spark and gpu) H2O (working with spark and GPU, support tensor, mxnet, and cafe) JcuDNN (JNI wrapping nvidia cuDNN) Mahout (hadoop) Mllib Spark
- 32. Tools
- 35. GCP
- 37. How we applied Machine Learning
- 38. Process mining
- 39. We are using micro services Another problem
- 40. What data that we need??? How to Collect
- 42. Event Drive Architecture Service ServiceService Service Service Message queue (kafka) RecommedationFraud Service Machine Learning Engine
- 43. Cases
- 45. Anomaly Detection Anomalies are patterns in data that do not conform to a well defined notion of normal behavior These nonconforming patterns : outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains
- 46. Challanges Defining a normal region In many domains normal behavior keeps evolving Availability of labeled data for training/validation of models used by anomaly detection techniques is usually a major issue Often the data contains noise that tends to be similar to the actual anomalies
- 47. Anomaly Detection Solution Method : Gaussian MIxture Model Fitted by EM - Algorithm
- 48. Gaussian Distribution Before GMM, try to remember the gaussian distribution
- 50. Gaussian Mixture We have 3 gausians mean 3 clusters on the right picture. We have 4 gausians mean 4 clusters on the left picture.
- 52. Gaussian Mixture Multivariate We have more than one gaussian mixture mean we have more than one possible position for each data that we want to distribute to GMM. for example we have data x then want to trying to distribute x to GMM, then we need to calculate the probability of x in first gaussian, then in second gaussian, until our last gaussian. It mean we have p(x) given each gaussians parameters. f(x) = argmax{p(x | μ1, Σ1), p(x | μ2, Σ2), p(x | μ3, Σ3),... p(x | μn+1, Σn+1) } where p is : p(x | μ, Σ)
- 53. EM (Expectation Maximization) Algorithm • GMM Ref : https://brilliant.org/wiki/gaussian-mixture-model/
- 54. EM (Expectation Maximization) Algorithm , Dimana
- 55. Fraud Detection
- 56. Fraud Detection Payment Fraud (phishing, account take-over, carding) System abuse (promo, content, account, logistic and payment methods especially COD) Fraud not only result in financial losses but also produce some reputational risk. Some security measures has been taken by bank or another multinational finance service. [E. Duman et al, 2013]