AIML02 - A.I. and Machine Learning Applications: Connecting Concepts to Practice
1024522
Delivery Methods
Online (Live)
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Program Description
This online professional certificate program is designed for aspiring data scientists and machine learning enthusiasts. Learn fundamental concepts and apply A.I./ML to real-world datasets across various fields.
Who Should Attend
- Aspiring data scientists and machine learning enthusiasts who want to learn how to use Python to manipulate data and solve problems using A.I.
- Individuals looking to see how basic statistics, machine learning, and deep learning concepts are applied in real-world scenarios.
Learning Objectives
- Understand A.I. Basics: Distinguish between A.I. and non-A.I. applications
- Python Proficiency: Learn the basics of reading and using Python code
- Accelerated Libraries: Know when and how to use libraries like NumPy and Pandas for efficient data manipulation
- Data Cleaning: Identify and modify NumPy and Pandas code to clean data effectively
- Computational Modeling: Grasp the concepts of computational modeling and experimental design
- Data Visualization: Utilize libraries like Matplotlib and Seaborn to create various types of data visualizations
- Machine Learning Fundamentals: Understand the differences between classification and regression in machine learning
- Data Splitting: Learn how to split datasets into training and testing sets
- Bias and Variance: Comprehend the tradeoffs between bias and variance in machine learning models
- Classical Algorithms: Apply classical machine learning algorithms such as k-Nearest Neighbors, k-Means, and artificial neural networks
- Clustering Techniques: Identify and use clustering algorithms effectively
- Training vs. Inference: Understand the distinction between training and inference phases in machine learning
- Advanced Neural Networks: Leverage advanced deep neural networks, like CNNs, to solve computer vision problems
- Large Language Models: Understand the basics of large language models (LLMs) and their functionality
- Prompt Engineering: Use prompt engineering to generate answers using large language models
- Retrieval Augmented Generation: Learn how retrieval augmented generation systems work and their limitations
- A.I. Research: Effectively find, read, and understand A.I. research concepts from academic papers
- Practical Application: Integrate learned concepts to solve a self-selected problem
Earn a Badge
At the end of this certificate, you are eligible to earn a badge by completing and passing an assessment.
Instructors
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Dr. Derek Riley joined the MSOE faculty in 2016 and is a professor in the Computer Science and Software Engineering Department. He is also program director of MSOE’s Bachelor of Science in Computer Science program, which has a focus in artificial intelligence. In addition to teaching at MSOE, Riley provides consulting services and expert witness services related to machine learning, deep learning, facial recognition, computational modeling, high-performance computing, and other related fields. His areas of expertise include deep learning, machine learning, computer vision, algorithms, process modeling and simulation, Scrum, and mobile computing/programming. He is an NVIDIA DLI Certified Instructor.
Dr. Yiyang (Ian) Wang joined MSOE in 2023 and currently serves as an assistant professor in the Computer Science and Software Engineering Department. He received his Ph.D. in computer science from DePaul University in 2023. Ian's research interests encompass data science and machine learning, with a primary focus on their applications in the medical domain.