What is Machine Learning?

Machine learning is the force behind the scenes. It shapes the way we interact with our devices, guides recommendations on our streaming platforms, and influences healthcare diagnoses. These common occurrences show how machine learning is transforming technology. It improves, predicts, and adjusts in ways that were once considered science fiction.

Learn more about machine learning by exploring examples, understanding the difference between machine learning and artificial intelligence, and exploring career opportunities in the field.

What is the definition of machine learning?

Machine learning (ML) is a field within artificial intelligence (AI) that empowers computers to learn and make decisions without explicit programming. This learning process is driven by data, where the system identifies patterns, recognizes trends, and refines its performance over time. Three primary types of machine learning include:

• Supervised Learning: This model uses labeled datasets to train algorithms to predict outcomes and recognize patterns, allowing it to make predictions on new, unseen data.
• Unsupervised Learning: This model operates with unlabeled datasets and allows algorithms to uncover patterns and structures within the data without predefined output labels.
• Reinforcement Learning: In this model, an agent interacts with an environment, learning to make decisions by receiving feedback in the form of rewards or punishments.

What is the difference between machine learning and artificial intelligence?

While the terms are often used interchangeably, machine learning is a subset of AI. AI refers to the broader concept of creating machines that can perform tasks that typically require human intelligence. However, machine learning focuses on the development of algorithms that allow machines to learn from data. Machine learning is a means by which AI is achieved.

What are some examples of machine learning?

ML has appeared in various domains and has sparked changes in industries that have enriched our daily experiences. Here are some noteworthy examples:

1. Virtual Personal Assistants: Responding to voice commands, virtual personal assistants, like Siri and Alexa, not only retrieve information but also refine their responses based on your past interactions. Machine learning analyzes and adapts to your preferences, creating a personalized and efficient user experience.
2. Recommendation Systems: Entertainment and e-commerce giants like Netflix and Amazon leverage ML to enhance user experience through recommendation engines. By analyzing user preferences, these platforms provide personalized content and product suggestions.
3. Email Spam and Malware Filtering: Email clients employ various spam filtering approaches to ensure inbox security. Machine learning is continuously updating and enhancing these filters to identify and combat spam and malware threats.
4. Healthcare Diagnostics: Machine learning models play a crucial role in advancing diagnostics in healthcare. These models excel in medical image analysis, aiding in the detection of diseases and enabling the formulation of personalized treatment plans based on patient data.

Jobs in machine learning

AI and ML are being used in nearly every industry today, from entertainment to finance to customer service. According to Indeed, the position of a machine learning engineer ranks at #8 among the top jobs in the U.S. This showcases a 53% growth rate since 2020 and an average annual salary of $153,252.

According to Indeed and Glassdoor, here are some potential roles in ML:

• Machine Learning Engineer ($103,000-$255,000 salary range)
• AI Product Manager ($76,000-$187,000 salary range)
• Data Scientist ($80,000-$191,000 salary range)
• Natural Language Processing (NLP) Engineer ($122,000-$189,000 salary range)
• Computer Vision Engineer ($73,000-$217,000 salary range)

Education in machine learning

A Master of Engineering (MEng) degree can open a wide range of career opportunities in various industries where AI and machine learning are playing an increasingly important role.

The University of Illinois Chicago’s (UIC) online Master of Engineering with a focus area in AI and Machine Learning program offers a unique opportunity to dive headfirst into the cutting-edge world of artificial intelligence. The online program’s courses help students develop their understanding of the fundamental math of AI and ML, as well as AI and ML theories, techniques, and tools.

UIC Associate Professor Xinhua Zhang is the instructor for the CS 412: Intro to Machine Learning course. Dr. Zhang teaches students common machine learning tasks, such as classification, density estimation, and clustering. He also covers successful machine learning techniques and broader paradigms developed for these tasks. He encourages anyone intrigued by AI/ML to explore the online MEng program for a comprehensive understanding.

“The AI/ML industry will always seek talents who can not only proficiently leverage off-the-shelf toolboxes to implement a function, but also systematically understand their underlying mechanisms,” said Dr. Zhang. “This skill cannot be garnered by simply participating in bootcamps or browsing piecemeal online blogs/tutorials. The MEng program equips students with exactly such a background, preparing them for the rapidly changing skillset sought by the job market.”

Machine learning continues to shape the future of technology and offers opportunities for innovation and problem-solving across many industries. As our understanding and capabilities in this field evolve, the impact of machine learning on our daily lives and industries is only set to grow. Talk to a UIC enrollment specialist today to learn more about the online MEng program.