AI & ML for Leaders: Foundations & Lifecycle Frameworks

You learn the complete data lifecycle, from collection to improvement, and how to evaluate data quality for effective ML outcomes. You also compare building models from scratch with improving existing ones. 

Topics include: 

• Importance of data and data types 

• Data collection methods 

• The AI project lifecycle and analysis 

• Improving vs. creating ML solutions 

• Tennis case study on data improvement 

You understand how ML models learn, how to frame feasible ML tasks, and how data representation influences model behavior. You also explore multimodal systems. 

Topics include: 

• Framing AI tasks 

• Input–output model view 

• Feasible vs. impossible tasks 

• Supervised learning and model training 

• Multimodal AI systems and their applications 

You see how engineers use training and test data, interpret learning patterns, and refine models to address issues like underfitting and overfitting. 

Topics include: 

• Training vs. test data 

• Memorization vs. generalization 

• Accuracy and loss behavior over time 

• Training processes and classification examples 

• How engineers evaluate learning quality 

You learn how to assess model performance, identify failure cases, and apply improvement strategies such as augmentation, synthetic data, and transfer learning. 

Topics include: 

• Generalization, accuracy, and performance metrics 

• Failure case analysis 

• Tank Detection failure story 

• Handwritten digit recognition activity 

• Data augmentation and synthetic data 

• Transfer learning for limited data scenarios 

You explore ethical, legal, and human-centered considerations that support responsible AI deployment. 

Topics include: 

• Reproducibility and benchmarking 

• Reducing dataset bias 

• Legal and commercial considerations 

• Ethical and responsible ML practices 

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 course 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, non technical professionals and project managers working with ML-driven initiatives—especially within engineering and scientific environments.

Yes. Upon successful completion, participants receive a certificate from Michigan Engineering, 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.

Applicable taxes will be calculated and added at checkout in accordance with country/state regulations.