Question

Business Analytics -MBA What is machine learning? How does it differ from statistically learning? Give an example of each. Are both still relevant and important when making business decisions? Explain your answer. Write your responses in detail with EXAMPLES. Be sure to identify the source of your example in your posting. Your initial post should be of minimum of 300 words.

Answer 1

Ans

Machine learning

Machine learning is a form of artificial intelligence in which a machine can perform tasks without being explicitly programmed to do so machine learning algorithm typically requires a lot of data that’s “cleaned,” or structured and organized deliberately.The data may be labeled to give the machine a sense for what it’s looking at, i.e. “bird” or “not a bird.” Such machine learning is referred to as “supervised” and is the kind of machine learning that businesses are most likely to encounter in this day and age.The algorithm is refined over time until it achieves a high degree of accuracy. The algorithm can then be applied to entirely different data sets. For example, data sets that aren’t labeled “bird” or “not a bird.”

According to SAS The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results.”

Statistical learning

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data.

Statistical learning covers the entire spectrum of machine learning, from gaining knowledge, making predictions or decisions and constructing models from a set of labeled or unlabeled data. The entire process is stated in a statistical framework, with every assumption stated mathematically as a null or alternative hypothesis.

The statistical learning approach is the same as any other scientific discipline:

1. Observe a phenomenon

2. Construct a model of that phenomenon

3. Make predictions using this model

But in statistical machine learning, the entire process needs to be automated for a computer program to learn from it.

* Difference between statistical learning and machine learning

1) Both methods are data dependent. However, Statistical Learning relies on rule-based programming; it is formalized in the form of relationship between variables, where Machine Learning learns from data without explicitly programmed instructions.

2) Statistical Learning is based on a smaller dataset with a few attributes, compared to Machine Learning where it can learn from billions of observations and attributes.

3) Statistical Learning operates on assumptions, such as normality, no multicollinearity, homoscedasticity, etc. when Machine Learning is not as assumptions dependent and in most of the cases ignores them.

4) Statistical Learning is mostly about inferences, most of the idea is generated from the sample, population, and hypothesis, in comparison to Machine Learning which emphasizes predictions, supervised learning, unsupervised learning, and semi-supervised learning.

5) Statistical Learning is math intensive which is based on the coefficient estimator and requires a good understanding of your data. On the other hand, Machine Learning identifies patterns from your dataset through the iterations which require a way less of human effort.

* Some more examples of statistical learning are

A Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. The prediction is to be based on demographic, diet and clinical measurements for that patient.

B Predict the price of a stock in 6 months from now, on the basis of company performance measures and economic data.

C Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person’s blood.

D Identify the risk factors for prostate cancer, based on clinical and demographic variables.

* Some examples of machine learning

1 Virtual Personal Assistants

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice.

2 Predictions while Commuting

Traffic Predictions: We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic.

3 Videos Surveillance

Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense.

4 Social Media Services

From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits.

5 Online Customer Support

A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site. However, not every website has a live executive to answer your queries

Source of information

I have used secondary sourse and used many of websites and books for this and some of them are

#The Hundred-Page Machine Learning Book

Book by Andriy Burkov

#The Elements of Statistical Learning

Book by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

# bigdatamadesimple.com

And is based on my personal learning