In the field of computer vision, models have typically been trained to perform well on a specific task or dataset by maximizing performance on a validation set. However, this approach only represents a small part of the types of scenarios that are of interest in real-world applications. In recent years, there has been growing interest in exploring different approaches to learning that can be applied in more diverse and dynamic environments. These approaches, which include lifelong learning, continual learning, meta-learning, transfer learning, multi-task learning, and out-of-distribution generalization, aim to enable models to be more robust, efficient, versatile, and well-behaved in non-stationary settings. This graduate course will focus on these emerging learning paradigms and how they can be applied to computer vision and multimodal learning tasks.

This course focuses on building foundations and introducing recent advances in machine learning, and on developing skills for performing research to advance the state of the art in machine learning. This course builds upon basic concepts in machine learning and additionally assumes familiarity with fundamental concepts in optimization and math. The course covers foundations and advanced topics in probability, statistical machine learning, supervised and unsupervised learning, deep neural networks, optimization, reinforcement learning, and causality. Students will be engaged through coursework, assignments, and projects.

This course is designed to explore recent breakthroughs in machine learning and provide students with the necessary skills to conduct research and advance the field of machine learning. It will cover highly specialized topics related to large-scale optimization for real-world problems, including Large-Scale Training of Kernel Methods, Sparse Learning, Bilevel Optimization, Black Box Optimization, and Spiking Neural Networks. Prior knowledge of fundamental concepts in machine learning, optimization, and statistics is assumed.

The study of probabilistic and statistical inference deals with the process of drawing useful conclusions about data populations or scientific truths from uncertain and noisy data. This course will cover some highly specialized topics related to statistical inference and their application to real-world problems. The main topics covered in this course are latent variable learning, kernel methods and approximate probabilistic inference strategies. This course will provide an in-depth treatment to various learning techniques (likelihood, Bayesian and max-margin) and numerous practical complexities (missing data, observed and unobserved confounding, biases) for performing inference.

The course covers advanced topics in continuous optimization, such as stochastic gradient descent and its variants, methods that use more than first-order information, primal-dual methods, and methods for composite problems. Participants will read the current state-of-the-art relevant literature and prepare presentations to the other students. Participants will explore how the presented methods work for optimization problems that arise in various fields of Machine Learning and test them in real-world optimization formulations to get a deeper understanding of the challenges being discussed.

The course covers advanced topics in Reinforcement Learning (RL). Participants will read the current state-of-the-art relevant literature and prepare presentations to the other students. Participants will explore how the presented methods work in simplified computing environments to get a deeper understanding of the challenges that are being discussed. Topics discussed include exploration, imitation learning, hierarchical RL, multi agent RL in both competitive and collaborative setting. The course will also explore multitask and transfer learning in RL setting.

This is a graduate course in a new branch of machine learning: federated learning (FL). In FL, machine learning models are trained on mobile devices with an explicit effort to preserve the privacy of users’ data. FL combines supervised machine learning, privacy, distributed and edge computing, optimization, communication compression, and systems. This is a new and fast-growing field with few theoretical results and early production systems (e.g., Tensor Flow Federated and FedML). This course aims for students to become familiar with the field's key developments and practices, namely optimization methods for FL and techniques to address communication bottlenecks, systems and data heterogeneities, client selection, robustness, fairness, personalization and privacy aspects of FL. The evaluation of the course heavily relies on students' paper presentations and the final project selected by the student.

In the past decades, interesting advances were made in machine learning, philosophy, and statistics for tackling long-standing causality problems, including how to discover causal knowledge from observational data, known as causal discovery, and how to infer the effect of interventions. Furthermore, it has recently been shown that the causal perspective may facilitate understanding and solving various machine learning / artificial intelligence problems such as transfer learning, semi-supervised learning, out-of-distribution prediction, disentanglement, and adversarial vulnerability. This course is concerned with understanding causality, learning causality from observational data, and using causality to tackle a large class of learning problems. The course will include topics like graphical models, causal inference, causal discovery, and counterfactual reasoning. It will also discuss how we can learn causal representations, perform transfer learning, and understand deep generative models.

This course is an advanced course on algorithms for big data that involves the use of randomized methods, such as sketching and sampling, to provide dimensionality reduction. It also discussed topics such as Sub-space Embeddings, Low rank Approximation, L1 Regression, Data Streams. The course lies at the intersection of machine learning and statistics.

This course focuses on recent advances in Natural Language Processing and on developing skills for performing research to advance the state of the art in Natural Language Processing.

This course focuses on recent topics in Natural Language Processing and on developing skills for performing research to advance the state of the art in Natural Language Processing.

This course focuses on developing knowledge about the state of the art in a wide range of Speech Processing tasks, and readiness for performing research to advance the state of the art in these topics.

The course introduces students to the emerging topic of natural language generation and prepares them to perform research to advance the state of the art in this research area.

PhD thesis research exposes students to cutting-edge and unsolved research problems, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of 3 to 4 years. PhD thesis research helps train graduates to become leaders in their chosen area of research through partly supervised study, eventually transforming them into researchers who can work independently or interdependently to carry out cutting edge research.

This course will prepare students to produce professional-quality research and solve a practical research challenge in an organization based on an innovative, sustainable, and entrepreneurial research topic. This course will provide exposure to a variety of special topics, research integrity, ethics, organizational challenges, and needs related to various disciplines. Students will design and implement a research project suitable for conference presentation or journal submission relevant to their field of interest, in addition to peer-reviewing a paper. The instructor, and guest lecturers, as appropriate, will present topics necessary to develop well-rounded researchers, innovators, and entrepreneurs in the AI disciplines.

PhD Internship (up to four months)