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Artificial Intelligence Machine Learning
Description: This course provides a comprehensive introduction to deep learning, a transformative branch of artificial intelligence powering technologies such as self-driving cars, virtual assistants, and personalized recommendation systems. Students will master the fundamentals of neural networks, optimization techniques, and activation functions while gaining hands-on experience with industry-standard frameworks including TensorFlow and PyTorch. The course explores advanced architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. Through labs and a semester-long research project, students will design and implement state-of-the-art models for real-world applications in computer vision, natural language processing, and related domains. By course completion, students will be able to analyze the assumptions and constraints of various deep learning models, evaluate cutting-edge research, and apply deep learning techniques to solve diverse problems. Letter grade only.
Units: 3
No sections currently offered.
Prerequisite: AIML 370
Artificial Intelligence Machine Learning
Term : Fall 2026
Catalog Year : 2026-2027
AIML 471 - Deep Learning
Description: This course provides a comprehensive introduction to deep learning, a transformative branch of artificial intelligence powering technologies such as self-driving cars, virtual assistants, and personalized recommendation systems. Students will master the fundamentals of neural networks, optimization techniques, and activation functions while gaining hands-on experience with industry-standard frameworks including TensorFlow and PyTorch. The course explores advanced architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. Through labs and a semester-long research project, students will design and implement state-of-the-art models for real-world applications in computer vision, natural language processing, and related domains. By course completion, students will be able to analyze the assumptions and constraints of various deep learning models, evaluate cutting-edge research, and apply deep learning techniques to solve diverse problems. Letter grade only.
Units: 3
No sections currently offered.
Prerequisite: AIML 370