AI and Machine Learning Applications: Connecting Concepts to Practice

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The Center for Professional Education (CPE) at MSOE is a leader in continuing education and training, creating opportunities for professional upskilling; earning industry-recognized credentials to support career advancement and increase earning potential; gaining technical knowledge in rapidly changing fields; and satisfying professional licensure requirements.

This professional certificate program is designed for aspiring data scientists and machine learning enthusiasts. Learn fundamental concepts and apply AI/ML to real-world datasets across various fields.

Dates: Wednesdays and Fridays, Oct. 16, 2024–Nov. 15, 2024 (5 weeks)
Time: 2–4 p.m. | CEUs: 2.0 | PDHs: 20 | Price: $1,499
Location: Online Synchronous
Instructors: Dr. Derek Riley, Dr. Ian Wang

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 AI.
• Individuals looking to see how basic statistics, machine learning, and deep learning concepts are applied in real-world scenarios.

Learning Objectives

• Understand AI Basics: Distinguish between AI and non-AI 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
• AI Research: Effectively find, read, and understand AI research concepts from academic papers
• Practical Application: Integrate learned concepts to solve a self-selected problem