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90 lines
5.0 KiB
90 lines
5.0 KiB
# Machine Learning in the Real World
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In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, Natural Language Processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for...what are the real world applications for these models?
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While a lot of interest in industry has been garnered by AI, which usually leverages Deep Learning, there are still valuable applications for classical machine learning models, some of which you use today, although you might not be aware of it. In this lesson, you'll explore how ten different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and thus more valuable to users.
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## [Pre-lecture quiz](link-to-quiz-app)
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## Finance
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One of the major consumers of classical machine learning models is the finance industry. Two specific examples we cover here are **credit card fraud detection** and **wealth management**.
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### Credit card fraud detection
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We learned about [k-means clustering](Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?
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K-means clustering comes in handy during a credit card fraud detection technique called **outlier detection**. Outliers, or deviations in observations about a set of data, can tell us if a credit card is being used in a normal capacity, or if something funky is going on. As shown in [this paper](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf), you can sort credit card data using a k-means clustering algorithm and assign each transaction to a cluster based on how much of an outlier it appears to be. Then, you can evaluate for riskiest cluster for fraudulent versus legitimate transactions.
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### Wealth management
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In wealth management, an individual or firm handles investments on behalf of their clients. Their job is to sustain and grow wealth in the long-term, so it is essential to choose investments that perform well.
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One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out [this paper](http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/) on evaluating fund performance using regression.
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## Education
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### Predicting student behavior
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### Preventing plagiarism
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### Course recommendations
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## Retail
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### Personalizing the customer journey
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### Inventory management
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## Health Care
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### Optimizing drug delivery
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### Hospital re-entry management
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### Disease management
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## Ecology and Green Tech
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### Forest management
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You learned about Reinforcement Learning in previous lessons. It can be very useful when trying to predict patterns in nature. In particular, it could be used to track ecological problems like forest fires and the spread of invasive species. In Canada, a group of researchers used Reinforcement Learning to build forest wildfire dynamics models from satellite images. Using an innovative "spatially spreading process (SSP)", they envisioned a forest fire as "fire is the agent at any cell in the landscape and the set of actions the fire can take from a location at any point in time includes spreading north, south, east, or west or not spreading. This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP) is a known function for immediate wildfire spread." Read more about the classic algorithms used by this group in this article: https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full
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### Motion sensing of animals
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### Energy Management
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This article discusses in detail how clustering and time series forecasting help predict future energy use in Ireland, based off of smart metering: https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf
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## Insurance
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### Actuarial tasks
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## Consumer Electronics
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### Motion sensing
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## Software
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### UI regression
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### Document search
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### Recommendation engines
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## Arts, Culture, and Literature
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### Fake news detection
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### Classifying artifacts
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## Marketing
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### 'Ad words'
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### Customer segmentation
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✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
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## 🚀Challenge
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Add a challenge for students to work on collaboratively in class to enhance the project
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## [Post-lecture quiz](link-to-quiz-app)
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## Review & Self Study
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**Assignment**: [A ML scavenger hunt](assignment.md)
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