This course examines how deep learning transforms art, aesthetics, and creativity. Through lectures and guest talks, students learn generative models, GANs, diffusion techniques, and computational aesthetics. It bridges technical foundations with artistic practice, highlighting AI’s role in augmenting human imagination while addressing ethical and philosophical questions of creativity.

This course introduces the foundations of deep learning, covering neural networks, optimization, sequence models, and generative architectures. Through lectures and practical labs, students gain hands‑on experience building models for vision, language, and reinforcement learning. It emphasizes both theoretical understanding and applied skills, preparing learners to innovate with AI.

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.

This course offers a practical introduction to neural networks and deep learning. It covers core concepts like backpropagation, optimization, and architectures, while emphasizing hands-on coding with PyTorch. Learners progress from fundamentals to advanced applications, gaining the skills to design, train, and evaluate models for real-world AI challenges.