Exploring Advanced Machine Learning Techniques for Professionals

Advanced machine learning techniques are powerful algorithms that use data to solve complex problems and make accurate predictions. Each algorithm has its own structure and purpose, and each is valuable to business leaders, across various industries, looking to conduct data analysis and make data-driven, informed decisions.¹

This post will touch on several advanced machine learning concepts, artificial intelligence (AI) techniques, and advanced neural network architectures.

Also known as supervised machine learning, this technique uses labeled data sets to train algorithms to classify data or predict outcomes accurately. Supervised learning is useful in building keenly accurate machine learning models.

According to IBM, “As input data is fed into a model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross-validation process. Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.”²

In data mining, supervised learning is useful in two types of problems:²

Classification uses an algorithm to assign test data accurately into categories. It recognizes particular entities within a dataset and strives to determine how to label or define them. Common classification algorithms include linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest.

Regression sheds light on the relationship between dependent and independent variables—often to make projections, such as for a business’ sales revenue. Common regression algorithms include linear regression, logistical regression, and polynomial regression.

In unsupervised learning, which is also known as unsupervised machine learning, algorithms analyze and group unlabeled data sets. They uncover hidden patterns or data groupings without requiring human intervention.

Unsupervised learning models are used for three key tasks:³

• Clustering groups unlabeled data based on similarities or differences; the groups are represented by structures or patterns in the information
• A rule-based method for determining relationships between variables in a dataset, association is often used for market basket analysis, which helps companies understand relationships between different products
• Helpful in the preprocessing data stage, dimensionality reduction lowers the number of data inputs to a manageable size while preserving the dataset’s integrity as much as possible

Because unsupervised learning can discover similarities and differences in information, it’s ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.³

Typically, a reinforcement learning agent—that is, the software entity being trained—can perceive and interpret its environment, take actions, and learn by trial and error.

Based on rewarding desired behaviors and punishing undesired ones, reinforcement learning trains software to complete certain desired actions. Through this important tool, agents learn to navigate the complex environments for which they were created—to control a video game, for instance, or to perform a specific task. With time and experience, through feedback that usually involves rewards and punishments, the agent learns and optimizes its actions.⁴

In this technique, knowledge gained through one task or dataset is used to improve model performance on another related task and/or different dataset.⁵ That is, it involves using what it learned in one setting to improve generalization in another.

Consider these differences between traditional learning and transfer learning processes:⁶

Traditional learning builds a new model for each new task, based on available labeled data. Algorithms assume that training and test data come from the same feature space, so if the data distribution changes or the trained model is applied to a new dataset, users must retrain a new model.

Transfer learning starts with a trained model or network and applies its knowledge, gained in an initial source task or data, toward a new, related target task or data. Transfer learning is especially useful in training deep learning models, due to the large amounts of data needed to create deep neural networks.⁷

This deep learning architecture trains two neural networks to compete against each other, striving to create more authentic new data from a given training dataset. If you have a database of images, for example, you could use a GAN to generate new images from it.

In 2022, IT specialist Casper Hansen wrote:⁸

“To understand GANs, you must first understand the terms generative and adversarial.

• Generative: You can think of the term generative as producing something. This can be taking some input images and producing an output with a twist. For example, you can transform a horse into a zebra with some degree of accuracy. The result depends on the input and how well-trained the layers are in the generative model for this use case.
• Adversarial: You can think of the term adversarial as pitting one thing against another thing. In the context of GANs, this means pitting the generative result (fake images) against the real images present in the data set. The specific mechanism is called a discriminator, which is implementing a model that tries to discriminate between the real and fake images.”

Mohammed Alhamid is a software engineer and director of the Centre of Healthcare Intelligence at King Faisal Specialist Hospital and Research Centre. In his 2022 article, “Ensemble Models: What Are They and When Should You Use Them?” he explained that, in machine learning predictions, “Models process given inputs and produce an outcome. The outcome is a prediction based on what pattern the models see during the training process. One model is not enough in many cases … Ensemble models are a machine learning approach to combine multiple other models in the prediction process. These models are referred to as base estimators. Ensemble models offer a solution to overcome the technical challenges of building a single estimator.”⁹

The article goes on to name those challenges: high variance, low accuracy, and the bias that can come in when using too few features to make a prediction.

“If we build and combine multiple models,” wrote Alhamid, “we have the chance to boost the overall accuracy. We then implement the combination of models by aggregating the output from each model with two objectives: reducing the model error and maintaining the model’s generalization.”⁹

Deep learning and artificial neural networks are AI methods that teach computers to process data in a way inspired by the human brain. Because deep learning models can recognize complex patterns in text, pictures, sounds, and other data, deep learning methods can automate specific tasks typically requiring human intelligence, such as describing imagery or transcribing sound files into text.^10,11

A neural network creates an adaptive system that computers can use to improve by learning from their mistakes. This heightens their accuracy in solving complicated problems, such as in document summarization or facial recognition.¹¹ Widely used advanced neural network architectures include those that follow here.

To explore Convolutional Neural Networks, also known as CovNets, consider how the human brain takes in visual information: Each neuron works in its own restricted region, called a receptive field, responding to stimuli only in that area. It’s connected to other neurons in a such a way that, together, they cover the entire visual field.¹²

Digital images are binary representations of visual data. Each contains a series of pixels arranged in a grid-like fashion. The grid contains pixel values denoting the brightness and color of each pixel.

CovNets are a class of neural networks that process data with grid-like topology, such as digital images. Like neurons in the human brain, each neuron in a CNN processes data only in its receptive field. To interpret visual information, it detects simpler patterns, such as lines and curves, first; it subsequently detects more complex patterns, such as faces and objects. By using a CNN, one can effectively enable computers to see.¹²

Recurrent Neural Networks are the only neural network with an internal memory. The state-of-the-art algorithm for sequential data, they’re used by Apple’s Siri and Google’s voice search.

As Matthew Urwin wrote for BuiltIn in 2024, “Because of their internal memory, RNNs can remember important things about the input they received, which allows them to be very precise in predicting what’s coming next. This is why they’re the preferred algorithm for sequential data like time series, speech, text, financial data, audio, video, weather and much more.”¹³ Recurrent neural networks can form a much deeper understanding of a sequence and its context than other algorithms can.

These neural networks learn context and understanding through sequential data analysis. Transformer models use an evolving set of mathematical techniques, typically called attention or self-attention, that helps them identify how distant data elements influence and depend on one another. In contrast to RNNs, transformers do not perform data processing in sequential order. This allows for greater parallelization and faster training.

Transformers play an important role in neural network designs that process sequences of text, sounds, genomic sequences, and time series data. The most common application of transformer neural networks is natural language processing (NLP), through which computers can interpret, manipulate, and understand human language, and it’s used to enhance Google’s search engine results.¹⁴

In the Online MS in Computer Science program at Chico State, you’ll take a deep dive into advanced topics that are valued in today’s job market: applied machine learning, software design, programming, AI, database management, and more. Learn from supportive, engaging faculty experts in a small-class environment and build the problem-solving skills that will serve you throughout your career.

Make today that day that it all starts. To learn more and review the admissions schedule and financial aid opportunities, schedule a call with an admissions outreach advisor.