Essential AI Techniques for Engineers equips participants with the fundamental and advanced skills necessary to implement machine learning algorithms effectively within engineering contexts. The course emphasizes a project-based learning approach, allowing participants to engage in hands-on activities that culminate in real-world applications. By the end of the program, attendees will have developed a robust understanding of AI methodologies and their integration into engineering solutions, enhancing their employability and practical expertise.
The curriculum is designed to provide a comprehensive exploration of essential AI techniques, focusing on practical applications that engineers encounter in various industries. Participants will work collaboratively on projects, fostering an interactive learning environment that encourages innovation and creativity. Additionally, results from these projects will be encouraged for publication in Cademix Magazine, providing participants with a platform to showcase their work and contribute to the broader engineering community.
Introduction to Machine Learning and AI Concepts
Supervised Learning: Regression and Classification Techniques
Unsupervised Learning: Clustering and Dimensionality Reduction
Neural Networks and Deep Learning Fundamentals
Model Evaluation and Performance Metrics
Feature Engineering and Data Preprocessing Techniques
Time Series Analysis and Forecasting with AI
Introduction to Reinforcement Learning
Practical Applications of AI in Engineering Projects
Final Project: Implementing an AI Solution to a Real-World Engineering Problem