Deep Learning Computer Vision Masterclass by Michigan Online

About this course

Developing reliable computer vision systems is no longer reserved for large research labs. With today’s tools and hardware, you can train models that recognize images, locate objects, segment scenes, and even learn meaningful representations from data—if you understand the foundations that make deep learning work. This course guides you from core principles to modern practices, connecting the why behind the math with the how of real-world model building.

You’ll start by framing the key ideas in image recognition, then build intuition for classifiers, optimization, and the training loop that powers neural networks. Instead of treating models like black boxes, you’ll learn how choices such as validation strategy, hyperparameters, activations, and learning rate schedules impact performance and stability. Along the way, you’ll strengthen your grasp of backpropagation and computational graphs so you can reason about gradients, debug training issues, and make improvements confidently.

As you progress, convolutional networks take center stage. You’ll explore why convolutions and nonlinearities are essential, how widely used CNN architectures evolved, and what residual connections accomplish when models get deep. Practical awareness of compute also matters: understanding why GPUs accelerate matrix operations helps you make better decisions about tooling, efficiency, and scaling experiments.

The course goes beyond classification to the broader computer vision toolkit. You’ll develop the mental model behind object detection outputs, the transition from detection to segmentation, and the alignment ideas that improved region-based methods. You’ll also gain perspective on 3D vision representations and how video data is commonly fed into deep networks, preparing you to work with temporal and spatial complexity.

To round out your skill set, you’ll touch on attention and sequence modeling concepts, learn how modern methods interpret and visualize what networks attend to, and build a clearer distinction between discriminative and generative modeling. You’ll see how objectives such as the KL term in VAEs shape learned representations, and you’ll get a practical overview of reinforcement learning goals and framing. By the end, you’ll have a cohesive understanding of the ideas that fueled the computer vision deep learning boom and the confidence to apply them to projects, portfolios, and advanced study.