Data Science Algorithms

3 credits · Graduate School

An applied and practical view of computational algorithms that are relevant for data science applications and infrastructure. Topics include optimization algorithms like momentum and RMSprop and their use in (stochastic) gradient descent-based methods, Neural Networks, and streaming algorithms for computational statistics, e.g., Monte-Carlo Markov Chain; Bayesian methods like Maximum Likelihood Estimation (MLE), and Maximum A Posteriori (MAP) are also discussed. [3]