AI & ML for Leaders: Foundations & Lifecycle Frameworks

Learn to analyze the full data lifecycle, apply practical methods for collecting and preparing high-quality data, and evaluate data challenges to strengthen the accuracy, reliability, and ethical use of AI/ML models.

Topics include:

1. The Importance of Data

• The Data Lifecycle in AI/ML Projects

• Understanding Data Types

• Methods of Data Collection

2. The AI Project Lifecycle and the Role of Analysis

• The AI Project Lifecycle

• The Role of Analysis

3. Improving an ML Solution vs. Creating One from Scratch

• Creating from Scratch

• Improving an Existing Model

• The Project Manager’s Balancing Act

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• Program Curriculum

4. The Tennis Case Study

• Reducing Unforced Errors

• Linking Back to AI

5. Wrap-Up: Data Acquisition and Processing

Learn to explain and analyze how AI and ML models are trained from concept to evaluation, identify key ML approaches and their real-world applications, and interpret model performance to understand results, limitations, and business impact.

Topics include:

1. Pre-Conceptualization of an AI Project

• Framing the AI Task

• The AI Project Manager’s Role

2. The Input–Output Viewpoint of an AI Model

• Common Data Types in AI Systems

• From Data Representation to Model Behavior

• Framing the Transformation

3. Framing Feasible AI Tasks

• From Definition to Design

• Example: Predicting Temperature in Celsius

• Identifying Feasible and Unfeasible Tasks

4. Supervised Learning: Teaching the Model through Examples

• How Models Learn from Labeled Examples

• Training Data vs. Test Data

• Supervised Learning in Practice

5. Multimodal AI Systems

• Why Multimodal Learning Matters

• Examples of Multimodal AI Systems

• The Project Manager’s Role in Multimodal AI

6. Wrap-Up: Training AI/ML

Learn to explain how training and test data drive model development, interpret learning behavior to assess model effectiveness, and evaluate techniques that help engineers refine models and address issues such as underfitting and overfitting.

Topics include:

1. Training and Test Data: Learning from Practice

• Training Data vs. Test Data

• The Student Analogy: Holding Out Test Data

2. How a Machine Model Learns

• Memorization vs. Generalization

• Training vs. Test Accuracy Over Time

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3. How a Machine Learning Model is Trained

• The Training Process

• Binary Classification: A Training Example

4. Evaluating Model Learning Patterns

• How Models Behave During Training

• How Engineers Know When a Model Is Learning Well

Learn to understand the end-to-end AI and ML lifecycle, integrate models into real business workflows, and build a pipeline that supports continuous improvement and long-term impact.

Topics include:

1. The Big-Picture Approach

• Checking for Generalization: Training vs. Test Loss

• Measuring Performance: Accuracy and Other Metrics

• Analyzing Failure Cases

• Determining If the Model Is “Good Enough”

• What Needs Improvement?

2. The Tank Detection Failure Story

• Background

• The Unexpected Discovery

• Lesson Learned: The Importance of Proper Dataset Construction

3. Interactive Exercise: Handwritten Digit Recognition

4. Post-Evaluation Analysis-Guided Data Work

• Data Augmentation: Expanding the Dataset without Collecting New Data

• Using Model Analysis to Guide Data Collection

• Using Generative AI and Synthetic Data to Expand Training Data

5. What If There Isn’t Any New Data?

• Using Embeddings to Reduce Data Requirements

• Transfer Learning: Fine-Tuning Models with Less Data

6. Conclusion: Making AI Feasible with Less Data

Learn to understand key ethical considerations in AI and ML, apply human-centered design principles, and develop strategies for responsible, trustworthy deployment.

Topics include:

1. Key Considerations for an AI Technical Project Manager

• Reproducibility: Ensuring Consistency and Reliability

• Benchmarking: Setting the Right Performance Expectations

• Reducing Bias in Datasets: Building Fair and Ethical AI

• Terms of Use, Model Reuse, and Commercialization: Navigating AI’s Legal and

• Business Landscape

• Conclusion

This is not a coding‑heavy program. Instead, it focuses on understanding how models are designed, trained, evaluated, and deployed, so you can effectively lead, oversee, or partner with ML teams.

The program uses cross‑industry examples, including Tesla, customer‑support ML systems, AI failures, and bias scenarios. It also takes reference cases from companies like Microsoft and Amazon. These help illustrate real‑world challenges, trade‑offs, and lifecycle decisions.

Learners participate in instructor‑led sessions, guided analysis and practical exercises. Live webinars, discussions and hands‑on walkthroughs ensure strong engagement and applied learning.

You will be prepared to define AI problems, evaluate model performance, collaborate with technical teams, oversee ML development cycles, and support AI initiatives with confidence and strategic insight.

The program is ideal for business leaders, cross‑functional partners, nontechnical professionals, and project managers working with ML-driven initiatives—especially within engineering and scientific environments.

Yes. Upon successful completion of the prorgam, participants receive a certificate from Michigan Engineering Professional Education, recognizing expertise in AI & ML leadership foundations and lifecycle frameworks.

No. Industry examples (e.g., Microsoft, Amazon) are included as supporting references and recommended readings rather than full, in‑depth case studies.

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