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1 - 5 of 5 results for: CS 236

CS 236: Deep Generative Models

Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and flow models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, and reinforcement learning. Prerequisites: Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Proficiency in some programming language, preferably Python, required.
Terms: Aut | Units: 3

CS 402L: Beyond Bits and Atoms - Lab (EDUC 211)

This lab course is a hands-on introduction to the prototyping and fabrication of tangible, interactive technologies, with a special focus on learning and education. (No prior prototyping experience required.) It focuses on the design and prototyping of low-cost technologies that support learning in all contexts for a variety of diverse learners. You will be introduced to, and learn how to use state-of-the-art fabrication machines (3D printers, laser cutters, Go Go Boards, Sensors, etc.) to design educational toolkits, educational toys, science kits, and tangible user interfaces. The lab builds on the the theoretical and evidence-based foundations explored in the EDUC 236 / CS 402 Practicum. Interested students must also register for either EDUC 236 or CS 402, complete the application at bit.ly/BBA-Winter2020 by January 4 at 5 p.m., and come to the first class at 8:30 a.m. in CERAS 108.
Terms: Win | Units: 1-3

EDUC 211: Beyond Bits and Atoms - Lab (CS 402L)

This lab course is a hands-on introduction to the prototyping and fabrication of tangible, interactive technologies, with a special focus on learning and education. (No prior prototyping experience required.) It focuses on the design and prototyping of low-cost technologies that support learning in all contexts for a variety of diverse learners. You will be introduced to, and learn how to use state-of-the-art fabrication machines (3D printers, laser cutters, Go Go Boards, Sensors, etc.) to design educational toolkits, educational toys, science kits, and tangible user interfaces. The lab builds on the the theoretical and evidence-based foundations explored in the EDUC 236 / CS 402 Practicum. Interested students must also register for either EDUC 236 or CS 402, complete the application at bit.ly/BBA-Winter2020 by January 4 at 5 p.m., and come to the first class at 8:30 a.m. in CERAS 108.
Terms: Win | Units: 1-3

EDUC 236: Beyond Bits and Atoms: Designing Technological Tools (CS 402)

This course is a practicum in the design of technology-enabled curricula and hands-on learning environments. It focuses on the theories, concepts, and practices necessary to design effective, low-cost educational technologies that support learning in all contexts for a variety of diverse learners. We will explore theories and design frameworks from constructivist and constructionist learning perspectives, as well as the lenses of critical pedagogy, Universal Design for Learning (UDL), and interaction design for children. The course will concretize theories, concepts, and practices in weekly presentations (including examples) from industry experts with significant backgrounds and proven expertise in designing successful, evidence-based, educational technology products. The Practicum provides the design foundation for EDUC 211 / CS 402 L, a hands-on lab focused on introductory prototyping and the fabrication of incipient interactive, educational technologies. (No prior prototyping experience required.) Interested students must also register for either EDUC 211 or CS 402L, complete the application at bit.ly/BBA-Winter2020 by January 4 at 5 p.m., and come to the first class at 8:30 a.m. in CERAS 108.
Terms: Win | Units: 3-4

GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B)

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Stude more »
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Terms: Aut | Units: 3
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