AI Project Management and AI System Engineering: Methods, Tools and Techniques

Categories: AI & IT
Wishlist

About Course

AI Project Management and AI System Engineering: Methods, Tools, and Techniques is a cutting-edge course designed to equip learners with the interdisciplinary skills needed to lead and engineer AI-driven projects. From understanding what artificial intelligence truly means to diving deep into its transformative sub-fields—such as machine learning, natural language processing, and computer vision—this course provides a comprehensive foundation in both the strategic and technical aspects of AI development. Whether you’re managing AI initiatives or building intelligent systems, you’ll explore how to harness AI’s potential effectively and responsibly.

In a world rapidly being reshaped by artificial intelligence, project leaders and engineers must understand not only how AI systems work but also how to manage data lifecycles, evaluate algorithmic performance, and integrate AI into real-world products and services. You’ll gain hands-on insights into managing AI projects from ideation to deployment, explore AI system architectures, and use industry-standard tools, frameworks, and cloud platforms. The course also explores the dual-use nature of AI, ethical implications, and emerging risks—preparing learners to build AI that is not just powerful but trustworthy.

Show More

What Will You Learn?

  • Understand the core concepts and real-world applications of AI
  • Manage AI projects using proven methodologies and lifecycle techniques
  • Engineer AI systems with scalable architectures and performance metrics
  • Apply machine learning techniques, including supervised, unsupervised, and reinforcement learning
  • Conduct data preprocessing, model training, and evaluation workflows
  • Use modern AI programming languages such as Python and R
  • Explore AI software tools and frameworks like TensorFlow, PyTorch, and Scikit-learn
  • Deploy AI models in the cloud using platforms like AWS, Azure, and GCP
  • Evaluate AI’s societal impacts, threats, and dual-use implications

Course Content

Introduction to AI
Introduction to AI This section introduces students to the transformative power of artificial intelligence, outlining its core principles, history, and the reasons behind its rise as a key enabler of technological innovation across industries. What is AI? Students explore the foundational concepts that define AI, including intelligence simulation, perception, reasoning, and decision-making, gaining clarity on how machines can perform tasks typically requiring human intelligence.

  • What is AI?
    00:00

AI Applications
AI Applications This section showcases real-world AI implementations across healthcare, finance, manufacturing, automotive, and entertainment, emphasizing AI’s ability to optimize, automate, and enhance decision-making processes. Major Sub-Fields of Artificial Intelligence Learners dive into the key sub-fields of AI—such as machine learning, natural language processing, robotics, and computer vision—understanding how each area contributes to intelligent system development

AI Project Management
This section equips learners with the methodologies and frameworks to plan, monitor, and execute AI projects, from scoping and resourcing to risk management and performance tracking.

AI System Engineering
AI System Engineering Here, students understand how to architect AI systems with a focus on modular design, scalability, integration, testing, and lifecycle management tailored to intelligent technologies. Machine Learning Models An overview of core machine learning paradigms, this section explains how models learn from data, highlighting key differences between supervised, unsupervised, and reinforcement learning techniques. Regression Algorithms Learners explore how regression models—such as linear and polynomial regression—are used to predict continuous outcomes and uncover relationships between variables in AI systems. Classification Algorithms This section introduces classification methods like decision trees, support vector machines, and logistic regression, used to categorize data and make binary or multi-class predictions. Unsupervised Learning Students learn about clustering and dimensionality reduction techniques, such as K-means and PCA, which help identify patterns in unlabeled data and extract insights from complex datasets. Reinforcement Learning This topic covers how agents learn optimal strategies through trial-and-error interactions with environments, with applications in robotics, game AI, and autonomous systems. Machine Learning Modelling Lifecycle An end-to-end view of how machine learning models are conceptualized, built, validated, and deployed, emphasizing iterative development and continuous improvement. Data Preprocessing and Data Wrangling Learners acquire skills to clean, transform, and structure raw data into usable formats, a crucial step for enhancing model performance and reliability. Data Gathering and Preparation This section focuses on sourcing, annotating, and curating high-quality datasets, including ethical considerations and data governance best practices. Training Machine Learning Models Students learn how to feed data into models, fine-tune parameters, and optimize performance using training, validation, and testing pipelines. AI System Performance Evaluation Here, learners explore evaluation metrics like precision, recall, F1-score, and ROC curves to assess model accuracy, robustness, and fairness. Programming Languages for AI The course introduces programming languages widely used in AI—such as Python, R, and Julia—highlighting their ecosystem, strengths, and usage in development workflows. AI Software Tools and Frameworks This section presents popular AI libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras, with guidance on how to apply them in real-world projects. AI Hardware Learners gain an understanding of specialized hardware like GPUs, TPUs, and neuromorphic chips that accelerate AI workloads and support computational demands. AI/ML in Cloud Students explore cloud-based platforms (e.g., AWS SageMaker, Google AI Platform, Azure ML) that provide scalable environments for training, deploying, and managing AI models

AI is Dual Use Technollogy
AI as Dual-Use Technology This section discusses how AI can be used for both civilian and military purposes, raising awareness about ethical challenges, misuse risks, and the need for governance. AI Threats Learners are introduced to emerging risks in AI, including bias, adversarial attacks, job displacement, and surveillance concerns, alongside mitigation strategies.

Education in Future Technologies

wpChatIcon
    wpChatIcon