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Application closes 12th Dec 2024
Curriculum designed and delivered by renowned JHU faculty
Monthly live online masterclasses by Johns Hopkins faculty
5 interactive live learning sessions with industry mentors
5 industry masterclasses from industry experts
Earn a certificate from Johns Hopkins University and 6 CEUs upon program completion
Personalized assistance from a dedicated program manager
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Program Features:
This program provides both theoretical knowledge and practical skills, ensuring that participants are well-equipped to drive AI initiatives and lead their organizations into the future.
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Elevate your expertise with our meticulously crafted AI Business Strategy program, designed to equip professionals with the strategic and practical knowledge necessary to lead AI-driven transformations. The curriculum has been developed by leading faculty from Johns Hopkins University, blending theoretical foundations with real-world applications to ensure a robust learning experience.
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Pre-work: Evolution of Data Science
Begin your journey with an introduction to the evolution of Data Science, its foundational concepts, and its impact across industries. This pre-work sets the stage for deeper exploration in subsequent courses.
Module 1: The AI Landscape (Week 1)
Understanding AI Fundamentals: Gain a comprehensive understanding of key concepts such as Artificial Intelligence (AI), Data Science (DS), Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI).
Demystifying AI: Cut through the hype surrounding these technologies to uncover how AI drives value by enhancing judgment, interaction, and automation within businesses.
Applying the R.O.A.D. Framework: Explore and apply the R.O.A.D. Framework for managing AI projects from inception to implementation, ensuring strategic alignment and successful outcomes.
Module 2: Machine Learning Fundamentals (Week 2)
Essentials of Machine Learning: Dive into the core concepts and terminology of Machine Learning, exploring different types of ML and their applications across various business domains.
Performance Metrics: Learn to calculate key ML performance measures such as precision, recall, and F1 score, and understand the trade-offs between these metrics.
Hypothesis Testing: Gain insights into hypothesis testing, including Type I and Type II errors, to equip yourself with the analytical skills needed for evaluating and predicting ML outcomes effectively.
Activity: Calculate the performance measures for various notional AI scenarios
Module 3: AI and ML Technology (Week 3)
Selecting AI Algorithms: Learn to select the most suitable AI algorithms for different business challenges by comparing their strengths, weaknesses, and trade-offs.
Exploring Key Algorithms: Deep dive into key AI algorithms such as Support Vector Machines, Naïve Bayes, Decision Trees, Random Forests, and Neural Networks.
Optimizing AI Solutions: Gain the knowledge to match the right algorithm to specific business needs and optimize AI-driven solutions for maximum impact.
Module 4: Optimizing Data for Business Success (Week 4)
Data Structures and Types: Get introduced to essential vocabulary related to data structures and types, including nominal, ordinal, and categorical data.
Ensuring Data Quality: Learn to calculate inter-annotator agreement and explore the trade-offs between data size, consistency, and quality.
Data Labeling and Cognitive Limits: Understand labeling techniques, cognitive limits, and terms of reference to assess and manage data quality effectively in AI projects.
Module 5: Optimizing AI Resources and Performance (Week 5)
Resource Allocation in AI: Focus on identifying and evaluating the trade-offs between resource allocation, system performance, and bias in Machine Learning and AI systems.
Balancing Performance and Fairness: Explore memory and computational trade-offs, query expressiveness, and performance considerations while learning to identify and mitigate sources of machine bias.
Complex Decision-Making: Navigate the complex decisions involved in optimizing AI systems to achieve both performance and fairness goals.
Project: IT Modernization for Prison Management Using AI
Module 6: Mitigating AI Bias and Risk (Week 6)
Understanding AI Bias: Explore the various sources of bias in Machine Learning and AI systems, including algorithmic, human, and measurement bias, as well as algorithmic drift.
Risk-Based Mitigation: Learn to identify and mitigate these biases using a risk-based approach combined with human oversight.
Legal and Ethical Responsibilities: Cover the fiscal, performance, privacy, and legal responsibilities tied to AI, equipping you to justify and navigate current laws and international regulations governing AI.
Module 7: Generative AI (Week 7)
Introduction to Generative AI: Delve into the theory and application of Generative AI, covering key technologies such as Convolutional Neural Networks, Transformers, and Large Language Models.
AI Models and Applications: Identify the fundamental differences between stochastic AI models and expert systems, gaining a deeper understanding of various AI approaches and their practical implications.
Module 8: Leading AI Revolution (Week 8)
AI Leadership and Team Dynamics: Focus on estimating and organizing the people, roles, and responsibilities needed for successful AI projects.
Assessing and Building Teams: Explore common roles and skills in AI, identify which roles can or should be automated, and understand the pace of labor transition.
Ensuring Meritocracy and Diversity: Assess AI team performance to ensure meritocracy and build cognitively diverse teams to enhance project outcomes.
Module 9: Designing Scalable AI Projects (Week 9)
Managing Large AI Projects: Learn a comprehensive framework for managing at-scale AI projects, covering each stage from value realization to continuous improvement.
Custom Solutions and Pricing: Focus on critical management concerns, including data foundations, governance, advanced insights, automation, and interaction. Equip yourself with skills to effectively plan and execute large-scale AI initiatives.
Module 10: Managing Large-Scale AI Implementations (Week 10)
Optimizing AI Delivery: Explore management solutions to optimize the delivery of at-scale AI projects and mitigate associated risks.
Risk Management: Identify potential risk sources and learn strategies for enterprise and system optimization, supported by delivery excellence teams.
Best Practices: Demonstrate sound management practices to ensure effective risk management and project success.
Final Project: Proposal Development for a Large-Scale AI Project
Note: Curriculum, projects, tools are under the purview of JHU and can be updated as per industry requirements
Implement your skills
Transform theoretical knowledge into tangible skills by working on multiple hands-on exercises under the guidance of industry experts.
Note: The listed projects and case studies serve as examples; actual content may vary as the program evolves.
Enhance your professional credentials with a certificate in AI Business Strategy from Johns Hopkins University, showcasing your expertise in AI-driven business innovation. Earn 6 Continuing Education Units (CEUs) upon program completion. Share your achievement with your network and elevate your career in the rapidly evolving AI landscape.
* Image for illustration only. Certificate subject to change.
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For any feedback & queries regarding the program, please reach out to us at office-aibs-gl@jhu.edu
When you choose the AI Strategy for Business program from Johns Hopkins University, you gain access to world-class coaching from renowned faculty and industry experts.
Note: This is an indicative list and is subject to change based on the availability of faculty and mentors
Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.
Apply by filling a simple online application form.
Your application will be reviewed by the admissions team.
Selected candidates will receive an offer letter. Secure your seat by paying the admission fee.