The Big Advantage with Small Data | Northeastern Online скачать в хорошем качестве

The Big Advantage with Small Data | Northeastern Online

"Every problem is a small data problem. It's not just about doing more with less—it's about actually making sure that AI helps areas it hasn't been able to before." Professor Sarah Ostadabbas and students from Northeastern University's Machine Learning with Small Data course share why small data ML is pushing the boundaries of what AI can do. Hear from students who discovered that impactful AI solutions don't necessarily rely on big data, learned to make meaningful impact even when data is very limited, applied thoughtful feature engineering to solve real problems, and connected academic learning to real-world challenges through hands-on projects. Professor Sarah Ostadabbas: "What's exciting about small data isn't just finding a workaround. It's about pushing the boundaries of AI and showing what machine learning can actually do. That's what keeps me hooked on small data—it never gets old." As Associate Professor of Electrical and Computer Engineering and Director of Women in Engineering at Northeastern University's College of Engineering, Professor Ostadabbas brings cutting-edge small data ML research directly into this course. Why Small Data Machine Learning Excites Students: Making AI Accessible in New Domains: Small data techniques enable AI applications in areas previously thought impossible—rare diseases, specialized manufacturing, emerging technologies, and more. Generative Models Breakthrough: "The advancement happening in generative models means we can expand synthetically the datasets we have and innovate and make an impact even when data is very limited." Thoughtful Problem-Solving: "Impactful AI solutions don't necessarily rely on this concept of big data. With the right models and thoughtful feature engineering, you can think about more interesting solutions to tackle the problem you're faced with." Human-Centered AI: "The idea of machine learning is not to replace humans, but help humans have easier lives and become better. How we conduct research is always to see what was done before and how we can improve on it." Building on Existing Knowledge: "We always want to exploit what was already done in the big data domain, and then apply it to small data." This approach leverages big data ML advancements while adapting them for data-constrained environments. Career Impact: Whether you're aspiring to innovate in healthcare, develop smarter military systems, solve specialized industry problems, or push AI into new domains, this course provides both theory and practical expertise—turning small data into a big advantage. Student Outcomes: Build AI solutions that work with limited data Apply thoughtful feature engineering to real problems Leverage generative models for synthetic data expansion Connect academic knowledge to industry challenges Develop human-centered AI that augments rather than replaces Make impact in domains where big data approaches fail Why This Course Stands Out: Most ML courses assume big data availability. This course teaches what to do when that assumption doesn't hold—preparing you for real-world constraints most practitioners face. Through hands-on projects, collaborative learning, and experiential focus, students gain skills immediately applicable to data-constrained challenges. The Philosophy: Machine learning should help humans have easier lives and become better. Small data ML makes AI accessible in domains previously excluded from the AI revolution—rare diseases, specialized applications, emerging technologies, and privacy-sensitive fields like healthcare. This is AI for areas that need it most but have the least data. Ready to turn small data into your big advantage? 🔗 Machine Learning with Small Data Part 1: https://www.coursera.org/learn/machin... 🔗 Machine Learning with Small Data Part 2: https://www.coursera.org/learn/machin... 🔗 Northeastern Online Programs: https://online.northeastern.edu/ Advance your career with industry-driven programs in business, AI, healthcare, and technology—designed for working professionals.