GPMS TRANSPORTAL APPLIED AI COURSES

• This course introduces the core principles of AI project management, specifically tailored for computer vision projects. Students will learn how to manage the lifecycle of AI projects, focusing on defining clear objectives, ensuring proper data collection, selecting the appropriate algorithms, and coordinating with cross-functional teams to deliver successful computer vision solutions. The course will also cover managing timelines, handling stakeholder expectations, and measuring project success in AI-driven environments.
• Level: Intermediate
• Prerequisites: Basic understanding of AI concepts, no coding knowledge required.
• Assessments: Weekly Micro Assessments, Final Project-based Assessment.
• Job Roles Applicability: AI Project Manager, Technical Lead, Product Manager, Computer Vision Specialist.

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Week 1 - Introduction to AI Project Management

Learning Outcome

Learning Outcome: Understand the fundamentals of project management applied to AI, with an emphasis on computer vision projects.

1.1 Key elements of AI project management (scope, resources, timeline).
1.2 Understanding project lifecycle from planning to execution in computer vision.
1.3 Roles and responsibilities of a project manager in AI projects.

Practical Component
Create a project plan for a simple computer vision-based project (e.g., defect detection in manufacturing).

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Week 2 - Defining Clear Project Objectives and Scope in AI Projects

Learning Outcome

Learn how to define the scope and objectives for computer vision projects, ensuring alignment with business goals.

2.1 Identifying business needs and translating them into technical requirements.
2.2 Creating project milestones for computer vision applications.
2.3 Setting measurable outcomes and KPIs for AI projects.

Practical Component
Draft a project charter for a computer vision project, including defined objectives and measurable KPIs.

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Week 3 - Data Collection, Preparation, and Management for Computer Vision

Learning Outcome

Understand the importance of data in computer vision projects and how to manage data collection and preprocessing.

3.1 Gathering and labeling image/video data for computer vision tasks.
3.2 Ensuring quality and accuracy of data to avoid project setbacks.
3.3 Managing large datasets for AI projects.

Practical Componenet
Develop a strategy for collecting and labeling data for a computer vision-based quality inspection system.

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Week 4 - Algorithm Selection and Model Training for Computer Vision Projects

Learning Outcome

Learn how to select appropriate algorithms and models based on project requirements, and understand model evaluation.

4.1 Understanding different computer vision algorithms (e.g., CNN, object detection, image segmentation).
4.2 Model selection strategies based on accuracy and efficiency.
4.3 Evaluating models and ensuring they meet project goals.

Practical Componenet
Choose and implement an appropriate computer vision algorithm for an image classification task.

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Week 5 - Managing Project Timelines and Resources for Computer Vision

Learning Outcome

Explore project timelines, resource allocation, and managing stakeholders in AI-based computer vision projects.

5.1 Defining realistic timelines and resource requirements.
5.2 Coordinating with cross-functional teams (e.g., data scientists, engineers, business stakeholders).
5.3 Addressing challenges in computer vision project timelines (e.g., data collection delays, algorithm performance).

Practical Component
Create a project schedule with milestones, deadlines, and resource allocation for a computer vision application.

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Week 6 - Final Project and Assessment

Learning Outcome

Apply project management principles to a real-world computer vision project, from conception to delivery.

6.1 Develop a project roadmap for a computer vision project (e.g., facial recognition for access control).
6.2 Present the project plan, including scope, timeline, data strategy, and model evaluation.
6.3 Peer reviews and feedback on project management strategies.

Practical Component
Final project presentation and delivery, demonstrating project planning and execution for a computer vision use case.

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