The truth is in your data. The challenge is actually finding it.
Leading organizations are increasingly turning to machine learning and AI to mine their data. Machine learning leads to recommendation engines and streamlined operations, while AI can serve as decision augmentation. As a leader, you can harness their power to enhance critical decisions.
Rensselaer’s Machine Learning and AI Graduate Certificate prepares you to identify problems before they are apparent, get even closer to your customers’ true needs, and move far more quickly than competitors.
- Machine Learning and AI are built on the foundation of data analytics. This certificate requires a demonstrable professional or academic background in analytics, or the prior completion of either the Business Intelligence Certificate or the Production Analytics Certificate.
- You can begin the certificate in January, May, or August.
- All courses are delivered using Rensselaer’s digital classroom – the RensselaerStudio.
- Certificates are 9 credit hours, and can be completed in a year or less.
- Complete projects that address real-world business challenges.
- Master techniques and abilities that can be leveraged to elevate your role at work.
- Projects involve the context of your work, helping you to perfect your abilities while simultaneously providing value back to your employer.
- Faculty Practitioners provide industry expertise, advice, mentorship, and encouragement.
- Students have the option of completing just one certificate, or combining multiple certificates into a customized master’s degree.
- All certificates are offered for graduate-level credit, and require admission to Rensselaer’s graduate programs.
- Certificates are designed so that you can fully participate in classes from anywhere in the world.
The Machine Learning and AI Certificate requires three courses:
ENGR 6220: Data Architecture - Design and deploy systems that serve as the basis for the machine learning process. Choose appropriate learning models, using decision tree, Bayesian, neural network and vector machine approaches. Use multiple statistical approaches to evaluate output that leads to best results.
ENGR 6221: Machine Learning Frameworks - Develop predictive models for the least likelihood of unintended variance and build natural language and recommendation engines for common applications such as sales enhancement. Survey results and tune recommendation models to achieve more accurate predictions and determine best next steps.
ENGR 6222: Deep Learning in AI Systems - Working directly with a Faculty Practitioner, build machine learning systems that can be used for decision making intelligence, where learning systems transition from making recommendations to having decision-making capacities. Over the semester, propose and develop the model, and train the system to improve performance.