Machine learning’s powerful technology blends the best of human creativity and empathy with computer-driven efficiency, unlocking powerful opportunities for innovation. As machine learning (ML) transforms technologies and entire industries, there is an increased need for tech-driven professionals to uncover the most influential algorithms and insights of tomorrow. This begins with mastering machine learning basics: the fundamental theories and programming languages that drive transformative systems.
Read on to learn how a computer science degree can provide a valuable introduction to machine learning and the chance to embrace cutting-edge technologies.
Introduction to Machine Learning
Machine learning mimics the human process of acquiring new skills and knowledge. These systems are trained using large data sets — with advanced algorithms recognizing patterns that appear within high volumes of data. Trained models are then fully prepared to make data-driven predictions but also hold the capacity to continuously improve, using additional data to refine predictions and grow more accurate over time.
The U.S. Department of Energy describes ML as the “process of using computers to detect patterns in massive datasets and then make predictions based on what the computer learns from those patterns.” As a subset of artificial intelligence, ML promises to turbocharge innovation, moving beyond the need for direct human supervision or manipulation. This, in turn, could prompt dramatic improvement in efficiency while elevating data-driven decision-making as well as large-scale innovation.
Types of Machine Learning
Machine learning takes many forms and, like the AI umbrella under which it falls, includes numerous subcategories meant to tackle different challenges by using data (and training algorithms) in unique ways. These subcategories primarily relate to how data is collected and labeled, with common classifications including supervised and unsupervised learning.
Supervised Learning
Drawing on labeled data sets, supervised learning ensures that every input has a corresponding output. These input-output pairs are then used to train algorithms. Models uncover relationships between inputs and outputs, drawing on those insights to make predictions when presented with new data.
Supervised learning works well for classification tasks, which involve predefined categories for sorting data. In addition, supervised learning may call for regression, in which continuous numerical values drive predictions according to independent features. Because it requires labeled data, however, supervised learning can present a few noteworthy limitations. Chief among these is the extensive time and resources needed to collect or annotate high volumes of data.
Unsupervised Learning
Making the most of raw or unlabeled data, unsupervised learning moves beyond predefined labels, relying instead on techniques such as clustering to demonstrate notable relationships within sizable data sets. This can be useful for uncovering hidden patterns that might be difficult to pinpoint when important relationships exist but do not clearly align with predefined labels. Unsupervised learning is often used to detect anomalies and can also influence customer segmentation.
While unsupervised learning offers numerous advantages, its predictions may not be as precise as those driven by supervised learning and its clearly labeled input-output pairs. As a result, it may be more difficult to fully understand the patterns uncovered by this technology.
Other Learning Paradigms
While machine learning is often categorized based on the use of labeling (or lack thereof), other factors can determine how advanced models learn or make decisions. For example, semi-supervised learning can bridge the gap between supervised and unsupervised strategies, leveraging some amount of data alongside sets of unlabeled data. Meanwhile, self-supervised learning allows models to generate their own labels — elevating deep learning applications without requiring extensive human manipulation.
How Machine Learning Works: Key Concepts for Computer Scientists
Machine learning is shaped by data and advanced algorithms. Human expertise determines how these algorithms are developed and fine-tuned, but ultimately, ML effectiveness depends on the quality of the data used to train key models. Core aspects of ML systems include:
Data Collection and Preprocessing
Data collection ensures that machine learning models draw on a wealth of relevant information. This begins with defining objectives and selecting data sources based on identified goals. From academic resources to custom surveys and even web scraping, a variety of sources can contribute to this effort. Upon verifying that collected data is accurate and relevant, preprocessing ensures that this data is also suitable for machine learning. This may involve standardizing data or removing duplicates.
Feature Engineering
Feature engineering transforms raw data into relevant information known as features. This process is highly context-dependent, calling for an in-depth understanding of the data in question, along with the problems this information is supposed to address.
Various methods and techniques can be used to support feature engineering. Binning, for example, allows numerical values to become categorical features, comparing specific, individual values to the many other, surrounding values. Meanwhile, feature extraction simplifies data processing while retaining essential information. This transforms raw data into numerical features that can easily be used by computerized systems.
Model Training and Evaluation
Model training involves ‘teaching’ machine learning systems, determining how they recognize patterns and, in turn, using these insights to make predictions. This is where previously discussed strategies like supervised and unsupervised learning come into play.
Model evaluation is crucial, too, as this determines the performance of any given ML model. This strategic approach to machine learning assessment highlights the numerous strengths and weaknesses associated with each model, offering much-needed insights into model reliability. This may shape fine-tuning efforts and ultimately drive optimizations.
Overfitting and Underfitting
Many concerns can spark poor performance in seemingly advanced machine learning systems. Two of the biggest culprits? Overfitting and underfitting. These terms describe the extent to which machine learning systems are trained. Overfitting occurs when models are presented with too much information, while underfitting involves simplistic models that are unable to capture accurate insights based on limited data.
Such issues can often be avoided by striking the right bias-variance tradeoff. This involves an optimal balance between a model’s complexity and its ability to accurately learn from the data. Excessive bias tends to prompt underfitting, while high variance may lead to overfitting.
Practical Applications of Machine Learning
Machine learning and its diversity have a pivotal part to play in driving innovation across a wide range of industries. Practical applications include:
Recommendation Systems
ML-powered recommendation systems offer personalized suggestions for products, services, or experiences based on insights from purchase history or market trends. These systems can drive greater customer satisfaction in retail and e-commerce but can also shape experiences with streaming services, hospitality websites, e-learning platforms, and beyond.
Image Recognition
Machine learning algorithms allow computerized systems to identify and categorize objectives or individuals featured in various images. This reflects the human perception of sight and may integrate machine vision systems, which use sensors to capture images that are then processed and analyzed.
Image recognition has proven invaluable in e-commerce and warehousing, transforming inventory management and quality control. Additionally, advanced image recognition can support physical security — for instance, analyzing video feeds for signs of unauthorized access.
Natural Language Processing (NLP)
Natural language processing draws on machine learning to understand and analyze human language. This can drive sentiment analysis, revealing whether text includes positive or negative responses. This can be useful when striving to gauge public opinions and drawing on responses submitted via social media or customer reviews. NLP also aids chatbots, which can be used to engage in meaningful interactions. Chatbots respond to user queries, thereby boosting customer service by offering quick and helpful answers.
Challenges and Ethical Considerations
Machine learning promises to drive exciting innovations that could benefit all corners of the economy and modern society: healthcare, manufacturing, e-commerce, and beyond. However, with the promise of machine learning comes significant considerations, including ethical implications. Challenges include:
- Bias. Machine learning has a tendency to amplify existing biases, especially when the historical data used to train models reflect social prejudices or inequities. These biases can be difficult to overcome, in part, because ML systems often lack transparency. This limits the ability to pinpoint sources of bias and hold perpetrators accountable.
- Data privacy. The vast datasets used to train machine learning models often contain proprietary or copyrighted information, which is sometimes scraped without explicit consent. A growing concern is the potential for models to inadvertently reproduce sensitive or copyrighted content from their training data, sometimes without proper attribution or written disclosure. Often referred to as hallucinated recall, this has sparked growing concern over whether AI systems are unintentionally violating privacy, data ownership rights, or copyright laws when they reproduce parts of their training data.
- Employment concerns. Many hardworking professionals fear that they will be displaced by AI, especially as ML handles the manual or repetitive tasks once assigned to human employees. There is no denying that a workplace shakeup is upon us, but how we navigate these inevitable changes will determine how ML influences labor and employment. With high-level training, ML can support meaningful employment and even produce new job opportunities that would otherwise not exist.
These issues may seem alarming, but computer scientists have the power to uncover compelling solutions while continuing to leverage the extraordinary advantages of AI. By prioritizing ethical practices — and following ethical frameworks — computer scientists can protect user privacy and limit bias, all while ushering in a new era of data-driven opportunities.
How a Computer Science Degree Can Prepare You for Machine Learning
As ML continues to play a greater role in our economy and our everyday lives, mastering this technology becomes increasingly important — namely for computer scientists, as there is already a widespread expectation that tech-driven professionals will understand and embrace ML systems. A computer science degree can make this learning curve easier, offering a solid theoretical foundation along with projects and simulations that help you apply these crucial contexts in problem-solving scenarios.
What You Will Learn
A graduate-level computer science degree will expose you to the theoretical concepts that underscore machine learning. Breadth courses cover theory, systems, and software, offering a solid foundation that will make complex ML concepts smoother to navigate.
Active learning opportunities encourage you to take a deeper dive into ML models, revealing exciting ways in which these can be applied to solve problems, boost efficiency, and drive innovation. Along the way, you will learn to integrate soft skills such as critical thinking and communication.
Unleash the Power of Machine Learning: Prepare for the Future With TAMU
As you prepare to enter the ML-driven economy of tomorrow, look to Texas A&M for inspiration and support. TAMU’s online Master of Computer Science empowers tech-focused graduate students, complete with a comprehensive yet tailored curriculum and opportunities for active learning. Get in touch today to learn more.