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Lecture
Learning from Probabilistic Models
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Related lectures (28)
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Quantum Phase Estimation
Explains the Quantum Phase Estimation (QPE) algorithm and its complexity using two registers and SWAP gates.
Quantum Algorithms: Complexity and Learning Insights
Explores quantum algorithms, their complexity, and applications in learning, highlighting key concepts and recent research findings.
Energy System Modelling: Overview and Optimization Problems
Covers energy system modelling, optimization, scenarios, predictions, complexities, and controversies in energy models.
Density of States and Bayesian Inference in Computational Mathematics
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Elements of Computational Complexity
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Linear Systems: Direct Methods
Explores linear systems, direct methods, Gauss elimination, LU decomposition, and computational complexity.
Bias-Variance Tradeoff in Machine Learning
Explores the Bias-Variance tradeoff in machine learning, emphasizing the balance between bias and variance in model predictions.
Complexity & Induction: Algorithms & Proofs
Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.
