Courses and Schedule

This is an advanced machine learning course that will focus on the fundamental principles and related tools for structured prediction.

In this course we will explore both the fundamentals and recent advances in the area of deep learning. Our focus will be on neural network-type models including convolutional neural networks and recurrent neural networks such as LSTMs.

The goal of this course is to present an overview of linear and multilinear algebra techniques for designing/analyzing ML algorithms and models, and to engage students with new research in the area.

This seminar-style course will focus on recent advances in the rapidly developing area of “foundation models”, i.e. large-scale neural network models (e.g., GPT-3, CLIP, DALL-e, etc) pretrained on very large, diverse datasets.

This graduate course is an introduction to the treatment of nonlinear differential equations, and more generally to the theory of dynamical systems. The objective is to introduce the student to the theory of dynamical systems and its applications.

This course is at the interface between game theory, optimization, and machine learning. It tries to understand how to learn models to play games. It will start with some quick notions of game theory to eventually delve into machine learning problems with game formulations such as GANs or Multi-agent RL. This course will also cover the optimization (a.k.a training) of such machine learning games.

This course aims to introduce the basic techniques of deep learning including feedforward neural networks, convolutional neural networks, and recurrent neural networks.

Computer Science (Sci) : Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning.

Introduction to foundational ideas in computational linguistics and natural language processing. Topics include formal language theory, probability theory, estimation and inference, and recursively defined models of language structure.

A seminar course on recent advances in research problems at the intersection of computer vision and natural language processing, such as caption based image retrieval, grounding referring expressions, image captioning, visual question answering, etc.

Basic elements of statistical learning algorithms. Examples of applications in data mining, nonlinear regression, and temporal data, and deep learning.

This course combines lectures and seminar-style discussions to cover the foundations of causality and topics like causal representation learning, causal structure discovery, causal abstraction (and its use in understanding large models).

This class aims to discuss these limitations and study methods to overcome them and enable agents capable of training autonomously, becoming learning and adapting systems that require little supervision.

This course provides a deep dive into essential mathematics for computer science and is designed to teach not just important mathematical tools but the skill of mathematical thought in the context of CS, including how to write advanced mathematical proofs.

This course presents modern mathematical models of lighting and the algorithms needed to solve them and generate beautiful realistic images. Both traditional numerical methods and modern machine learning-based approaches will be covered.

This course is for people with no experience is NLP and would like to see how it can be used for exciting data science applications.

Throughout the course, we take several concepts in NLU such as meaning or applications such as question answering, and study how the paradigm has shifted, what we gained with each paradigm shift, and what we lost. This course will also delve into how large language models like ChatGPT are built, and latest advances on how to train/use these models for the task at hand.

The course covers representation, inference and learning with graphical models; the topics at high level include directed and undirected graphical models; exact inference; approximate inference using deterministic optimization based methods, as well as stochastic sampling based methods; learning with complete and partial observations.

This is a graduate course, in which we will cover methods for trustworthy and ethical machine learning and AI, focusing on the technical perspective of methods that allow addressing current ethical issues. This course will bring analytic and technical precision to normative debates about the role that data science, machine learning, and artificial intelligence play in consequential decision-making in commerce, employment, finance, healthcare, education, policing, and other areas.

The aim of this course is to introduce some continuous mathematics that has become increasingly important in machine learning.

In this course, we will examine computational models of pragmatics and how NLP systems have been empirically evaluated for their pragmatic reasoning ability. We will discuss classical theories of formal semantics and pragmatics, as well as more recent statistical and neural models.