Courses and Schedule

This course explores the theoretical and practical aspects of how neural networks learn and generalize. The goal of this course is not to introduce deep learning architectures or algorithms. Instead, this course will focus on understanding the training dynamics of neural networks and how they generalize. We will discuss in detail the optimization challenges involved in training overparameterized models.

Overview of the influence of neuroscience and psychology on Artificial Intelligence (AI). Historical topics: perceptrons, the PDP framework, Hopfield nets, Boltzmann and Helmholtz machines, and the behaviourist origins of reinforcement learning. Modern topics: deep learning, attention, memory and consciousness. Emphasis on understanding the interdisciplinary foundations of modern AI.

The course will include 5 chapters, starting from basics (but not spending much time there) and moving toward the modern algorithms : Recall of probability basics, Basics of Bayesian statistics, Variational Inference, Monte Carlo Methods and Advanced Variational Inference.

Selected topics in Network Science, Graph Mining and Graph Learning, including patterns in real world networks, ranking and similarity measures for graphs, graph clustering and community mining techniques, and node classification and link prediction methods.

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.

This is a seminar course on multimodal vision and language research. Some recent examples of highly successful vision-language models include GPT-4(V) and Gemini. This course will teach you the modeling techniques behind such systems, what their shortcomings are, what kind of tasks and datasets they are trained and evaluated on etc.

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

This course will combine lectures and seminar-style paper discussions to cover both the foundations of causality and its intersection with machine learning, a fast-moving area that connects to out-of-distribution generalization, scientific discovery and even interpretability of 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 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.

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 course will teach students to recognize where and understand why ethical issues and policy questions can arise when applying data science to real world problems. It will focus on ways to conceptualize, measure, and mitigate bias in data-driven decision-making. 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. 

Recent years have shown that unintended discrimination arises naturally and frequently in the use of machine learning and algorithmic decision making. We will work systematically towards a technical understanding of this problem mindful of its social and legal context. 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. Students will learn to think critically about how to plan, execute, and evaluate a project with these concerns in mind, and how to cope with novel challenges for which there are often no easy answers or established solutions.

A seminar course on the evaluation of natural language processing systems. We will survey issues related to validity and reliability of NLP systems, focusing on current practices in the research community. What do researchers aim to capture in their measurements of NLP systems? Do current evaluations actually fulfill the measurement goals of the researchers? Are the evaluations reliable and trustworthy? The course will include student-led presentations and discussions, analyses of exising evaluations, and a final course project.