Machine Learning

Machine Learning (ML) is an extension of Artificial Intelligence (AI) where computers are programmed to make decisions based on events occuring in systems and processes (recognize changes) with least amont of human intervention. In machine learning, systems learn from existing data. This is in essence, enabling computers to create new programs using statistical models such as MCMC, and predicting the outcome or making corrections (risk minimization using ERM ) to existing processes.
Forecasting is used in many areas such as weather, short and long term financial performance, stock performance etc. In forecasting, statistical models are used forecast future outcomes. Based on the forecasted output humans make meaningful decisions. There is no autonomous learning involved. In machine learning, the forecasted values are used as the basis and for every new event. New values are predicted in a autonomous manner based on the newly learnt values. This continously goes on, and analysis can be made using multiple models.
In the world of robotics, the industrial robots perform tasks with greatest precision in a repetitive manner and operate in fixed set of rules (programs and hardware/software). The ones that are humanoid robots, have additional intelligence such as pattern recognition, obstacle avoidance, machine vision, independent decision making and are programmed to learn as they perform their duties. The modern day automobiles have automated options that use the concepts of machine learning with embedded hardware/software that assist in lane drift correction, automated stopping, collision mitigation and so on that are self managed. As the machine learning technology matures, there will be cars that will be totally self-driving automobiles with all mobile/wireless features embedded in them.
Machine Learning Venn Diagram [Refer 2]
Data Mining, Machine Learning - Venn Diagram, [2]
In current business operations, a good example is the involvement of humans in decision making based on use of BI tools to create new sales strategies, incentives, regional price corrections, inventory adjustments, SCM, and so on. By integrating OLTP systems, sales transaction data stores (database), BI tools and with statistical models based prediction, minimize risks using ERM, the various systems can correct themselves and perform all human tasks in an automated manner without human intervention. The goal of machine learning is to enable systems to constantly monitor changes, recognize patterns, learn new trends, use statistical models and make intelligent decisions.
Some of the basic steps of machine learning do exist in current systems, where code will analyze missing data patterns and try to manage data. It stops there in terms of sending warning emails about new trends or change in patterns. The management has to make manual corrections by performing statistical analysis, many semi-automated or manual interventions and corrections. As each step of the process is automated using learning code, we can achieve total automation and create learning machines that work in an autonomous manner.
The social media and ecommerce companies have tried to initiate the machine learning concepts by providing users with related content and products respectively. There are still gaps in many cases as the predictive outcomes are biased since they model trends using previous users patterns to promote sales or get higher "like" counts in social media content.
Machine Learning In Financial Institutes
Financial institutions/banks use machine learning to prevent fraud. The machine learning models analyze card holder's buying pattern constantly. When there is a extremely large amount of card purchase transaction, it flags the transaction to stop the transaction. At a high level following are the ML steps.
1. Create a ML Model to analyze card holder's buying pattern including the stores, resturants, on-line purchases, locations etc.
2. Set base thresholds and let ML Model analyze the pattern continuously and learn new patterns as time goes by.
3. Analyze each transaction based on existing ML-Model pattern for the customer and learn/update pattern.
4. Flag the transaction as questionable or fraudulent if it fails the customer purchasing pattern (amount, location, method of purchase etc.).
Further, if the same card was used to purchase a flight ticket, say from Philadelphia to San Francisco, it should now know (by machine learning) that after specific date/time there could be credit card charges in the destination city - San Francisco and around. Still ML-Models have to analyze the transaction. Now the complexities are, was the ticket one-way or two-way. This also creates new ways to analyze transaction with date/timestamps and locations.
Another scenario is, if the ticket was bought by some other card/mobile transaction, since there may not be joint data exchange between credit card systems/financial institutions, the transaction could be flagged as fraud or questionable. Some card companies call the customer to provide verification to validate the transaction at POS. All these actions will be based ML customer-purchasing pattern analysis.
Three Key Machine Learning Methods
1. Supervised learning - a planned approach with standard input with several predicted output (in robots, all possible motions or 3D co-ordinates are recorded and it can operate autonomously using the learnt models and alogrithms). Mathematically the best approach to supervised machine learning is SVM.
2. Unsupervised learning - learning constantly in an autonomous manner with all types inputs (known and unknown) and system is allowed to predict output based on trends, statistical models and algorithms. The system has be configured to all possible statistical models and algorithms. Two well known methods used in unsupervised learning are PCA and clustering.
3. Semi-supervised learning - a hybrid of supervised and unsupervised learning. A system is configured to all possible statistical models and learning algorithms. The system is monitored and supervised learning is applied when responses are not as expected. This type of learning is best for situations that are too complex or inputs are unknown, resulting in unpredictable outcomes.
Related Information:
1. IBM Machine Learning
2. Looking backwards, looking forwards: SAS, data mining, and machine learning
3. SAS Machine Learning
4. Azure - Microsoft machine Learning
5. Neural Network Overview
6. Medical Conceptual Analysis

Last Revised on: October 13th, 2021