Simulating Molecular Biology: Challenges and Opportunities

Related lectures (30)

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Explores Projection Pursuit Regression for nonlinear modeling and the trade-offs with interpretability in neural networks.

Covers energy system modelling, optimization, scenarios, predictions, complexities, and controversies in energy models.

Explores linear systems, direct methods, Gauss elimination, LU decomposition, and computational complexity.

Covers state space models and the expressivity of transformers in sequence copying tasks.

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

Explores the inefficiencies of the incorrect method for polynomial interpolation.

Explores GPUs' architecture, multithreading, and their role in machine learning, discussing limitations and future trends.

By the instructor Mark Silberstein explores accelerator-centric OS architecture and the role of CPUs in modern computers.

Explores isomorphic mapping of algorithms to hardware and assessing efficiency metrics in hardware design.

Covers the Subset Sum problem and the efficient LLL algorithm for finding solutions in lattice basis reduction.