Seminar on Machine Learning

Time and Location

Block Seminar: 22.02.2021 – 22.02.2021 (online)

Talks

• First-Order Optimization Methods I
• First-Order Optimization Methods II
• Second-Order Optimization Methods
• Introduction to Supervised Learning
• Linear Least-Squares
• Shrinkage Methods
• Cluster Analysis I
• Cluster Analysis II
• Dimension Reduction
• Feature Design and Selection
• Multi-Layer Perceptrons I
• Multi-Layer Perceptrons II
• Multi-Layer Perceptrons III
• Tree-based Learning Algorithms I
• Tree-based Learning Algorithms II

Literature

• Watt, Jeremy, Reza Borhani, and Aggelos Katsaggelos. Machine learning refined: foundations, algorithms, and applications. Cambridge University Press, 2020.
• Strang, Gilbert. Linear algebra and learning from data. Wellesley-Cambridge Press, 2019.
• Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009.
• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
• Audet, Charles, and Warren Hare. “Derivative-free and blackbox optimization.” (2017).
• Nocedal, Jorge, and Stephen Wright. Numerical optimization. Springer Science & Business Media, 2006.
• Saad, Yousef. Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics, 2003.