This course provides theoretical foundations for the design and mathematical analysis of efficient algorithms that can solve fundamental tasks relevant to data science. Fundamental statistical models are considered for such tasks and the aim to design efficient (polynomial-time) algorithms that achieve the strongest possible rigorous (statistical) guarantees under these models. Further, robustness is incorporated by considering adversarial (worst-case) components into these models.
As a teaching assistant, my role is to conduct exercise classes, correct weekly exercises, contribute to the lecture notes and provide feedback on the graded components of the course.
For more information, check out the official ETH Catalogue here.
The goal of the Computational Intelligence Lab is to enable graduate students to connect their mathematical background in linear algebra, analysis, probability, and optimization with their knowledge in machine learning to gain a deeper understanding of modern machine learning models and tools of great practical impact.
In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. This is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability
As a teaching assistant, my role is to design exercise sheets, conduct tutorial sessions on Theory of Recurrent Neural Networks and Generative Models.
For more information, check out the official ETH Catalogue here
This course provides theoretical foundations for the design and mathematical analysis of efficient algorithms that can solve fundamental tasks relevant to data science. Fundamental statistical models are considered for such tasks and the aim to design efficient (polynomial-time) algorithms that achieve the strongest possible rigorous (statistical) guarantees under these models. Further, robustness is incorporated by considering adversarial (worst-case) components into these models.
As a teaching assistant, my role is to conduct exercise classes, correct weekly exercises, contribute to the lecture notes and provide feedback on the graded components of the course.
For more information, check out the official ETH Catalogue here.