Tópicos Especiais em Matemática para Machine Learning - Mathematics for Machine Learning (TEMML0001)

Ementa: Aspectos matemáticos dos principais métodos de aprendizado de máquina, incluindo: Álgebra Linear, Geometria Analítica, Decomposição de Matrizes, Cálculo Vetorial, Probabilidade e Distribuição, Otimização Contínua.

Syllabus: Mathematical aspects of main machine learning methods, including: Linear Algebra, Analytic Geometry, Matrix Decompositions, Vector Calculus, Probability, and Distribution, Continuous Optimization

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Blitzstein, J. K., & Hwang, J. (2019). Introduction to probability (2nd ed.). CRC Press.

Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.

Murphy, K. P. (2022). Probabilistic machine learning: An introduction. MIT Press.

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning (2nd ed.). MIT Press.

Strang, G. (2023). Introduction to linear algebra (6th ed.). Wellesley-Cambridge Press.

Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.