30 April 2018

Structured Prediction

  • Graphical Models
    • Bayesian Networks 
    • Markov Networks
  • Inference Methods
    • Message Passing 
    • Integer Programs
    • Dynamic Programming
    • Variational Methods
  • Classical Discriminative Learning
    • Structured SVM 
    • Structured Perceptron
    • Conditional Random Fields
  • Non-Linear Approaches
    • Structured Random Forests
    • Deep Structured Prediction
  • More Complex Structures
    • Hierarchical Classification
    • Sequence Prediction/Generation
  • Application Areas
    • Computer Vision
    • Speech Recognition 
    • Natural Language Processing
    • Bioinformatics

20 April 2018

Consumer Protection

A few areas of consumer protection that provide for certain indicators of measure for rights of consumers, fair trading practices, competition, and accurate information in the marketplace:

  • Access
  • Complaints Handling
  • Dispute Resolution and Redress
  • Economic Interests
  • Education and Awareness
  • Empowerment Index
  • Protection Index
  • Fraud Detection
  • Governance and Participation
  • Information and Transparency
  • Verifiable Practices and Standards
  • Privacy and Data Security
  • Safety and Reliability
  • Product and Service Reviews

Identity and Access Management

  • OpenAM
  • OpenSSO
  • Shibboleth
  • OpenDJ
  • OpenIDM

A Few Machine Learning Use Cases in IAM:
  • Provisioning accounts and permissions management
  • Dynamic risk scoring
  • Identification of Friend or Foe
  • Fraud and Threat patterns via detection of anomalies
  • Feature Engineering (attributes, subjects, resources, environments, roles, entitlements)
  • Rule profiling using decision functions
  • Clustering to identify threshold patterns, excess, shared identity attributes, overlaps
  • Potential for use with blockchain for digital identity and trust
  • Deep identification with biometrics and fingerprints
  • Mining for visibility of IAM and Security Information and Event Management

18 April 2018

Consumer Behavior

Consumer spending behavior is directly correlated to household income that dictates disposable income. One can build a user profile of consumers with a set of attributes that could be contextualized towards specific market trends. Globally different regions have their own taxation. But, invariably to map an entire user behavior one would have to look at an entire calendar period - day, week, month, year. So, in UK this would pertain to the April-to-April tax year. By doing this one can obtain clearer set of patterns during bank holidays, weekends, weekdays, seasonal, social events, and other periods to glean on specific contextual behaviors.  Once an anonymized user is mapped to Y1 the following Y2, Y3, Yn could be mapped to discover historical trends. Using machine learning approaches like clustering can provide for a means of visualization of complex networks to identify churn, segmentation, and intents for conversion. Additionally, semantic enrichment could provide further context for answering specific data science questions and end-to-end predictive storytelling. From looking at big data standpoint it would certainly help to process batch and in stream mode. However, one would have to take into account the difference between processing and event time of recorded behavior as well as to maximize in-memory computation. The below highlight key indicators that could be analyzed for consumer behavior.
  • Economic conditions
  • Group/Social Influence
  • Historical Trends
  • Location-Awareness
  • Marketing Campaigns
  • Personal Preferences
  • Purchase Power
Additionally, the following could further add value in cyclical process to identify, discover, and understand:
  • Consumer Habits
  • Conversion Targets
  • Product Choices
  • Consumer Reviews and Ratings
  • Consumer Sentiments
  • Identifying and Predicting Churn, Segmentation, Price Optimization
  • Profiling for insights, forecasting, personalized promotions/offers/discounts
  • Consumer Experience
  • Consumer Price Index
  • Consumer Satisfaction Index
  • Consumer Protection
  • Market Trends
  • Consumer Interests - unconscious consumption
  • Consumer Intents - conscious search

4 April 2018

Feature Structure Goals in Spark

Classification & Regression
End Goal:
  • Column of type Double to represent Label
  • Column of type Vector (Sparse or Dense)
End Goal:
  • Column of Users
  • Column of Items
  • Column of Ratings
Unsupervised Learning
End Goal:
  • Column of Type Vector (Sparse or Dense)
Graph Analytics
End Goal:
  • DataFrame of Vertices
  • DataFrame of Edges

Gremlin Guide

Gremlin Guide