Courses and Schedule

Self-driving vehicles are poised to become one of the most pervasive and impactful applications of autonomy, and have received a great deal of attention recently.

This course considers problems in perception, navigation, planning and control, and their systems-level integration in the context of self-driving vehicles through an open-source curriculum for autonomy education that emphasizes hands-on experience. Integral to the course, students will collaborate to implement concepts covered in lecture on a low-cost autonomous vehicle with the goal of navigating a model town complete with roads, signage, traffic lights, obstacles, and citizens.

This seminar will explore how machine learning can be applied in fighting climate change. We will look at ways that machine learning can be used to help mitigate greenhouse gas emissions and adapt to the effects of climate change – via applications in electricity systems, buildings, transportation, agriculture, disaster response, and many other areas. Particular emphasis will be given to understanding exactly when machine learning is relevant and helpful, and how to go about scoping, developing, and deploying a project so that it has the intended impact.

In this course, we will study machine learning models including models for decision making. Massive datasets are now common and require scalable analysis tools. Machine learning provides such tools and is widely used for modelling problems across many fields including artificial intelligence, bioinformatics, finance, marketing, education, transportation, and health. In this context, we study how standard machine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) can be scaled to massive datasets using modern computation techniques (for example, computer clusters). In addition, we will discuss recent models for recommender systems as well as for decision making (including multi-arm bandits and reinforcement learning).

This course covers a selected set of topics in machine learning and data mining, with an emphasis on understanding the inner workings of the common algorithms. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, support vector machines, clustering, neural networks, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.

This is a seminar-style course, where the class as a whole will work together in running the course. In the first few lectures, I will provide an overview of LLMs and highlight the challenges. By the end of the course, you should be able to meaningfully contribute to cutting-edge research in natural language understanding.

System Representation as probabilistic graphical models, inference in graphical models, learning parameters from data.

Basic elements of statistical and symbolic learning algorithms. Examples of applications in data mining, pattern recognition, nonlinear regression, and time data.

Designing autonomous decision making systems is one of the longstanding goals of Artificial Intelligence. Such decision making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision making systems. By the end of this course, you will have a solid knowledge of the core challenges in designing RL systems and how to approach them.

Computer Science (Sci) : An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing.

While graphs are intuitively and naturally represented by vertices and edges, such representations are limited in terms of their analysis, both theoretically and practically (e.g., when implementing graph algorithms). A more powerful approach is yielded by representing them via appropriate matrices (e.g., adjacency, diffusion kernels, or graph Laplacians) that capture intrinsic relations between vertices over the "geometry" represented by the graph structure. Spectral graph theory leverages such matrices, and in particular their spectral and eigendecompositions, to study the properties of graphs and their underlying intrinsic structure. This study leads to surprising and elegant results, not only from a mathematical standpoint, but also in practice with tractable implementations used, e.g., in clustering, visualization, dimensionality reduction, and manifold learning, and geometric deep learning. Finally, since nearly any modern data nowadays can be modelled as a graph, either naturally (e.g., social networks) or via appropriate affinity measures, and therefore the notions and tools studied in this course provide a powerful framework for capturing and understanding data geometry in general.

This course provides a rigorous introduction to the field of machine learning (ML). The aim of the course is not just to teach how to use ML algorithms but also to explain why, how, and when these algorithms work. The course introduces fundamental algorithms in supervised learning and unsupervised learning from the first principles. The course, while covering several problems in machine learning like regression, classification, representation learning, dimensionality reduction, will introduce the core theory, which unifies all the algorithms.

The goal of this course is to introduce the concepts (theory and practice) needed to approach and solve data science problems. The first part of the course will cover the principles of analyzing data, the basics about different kinds of models and statistical inference. The second part expands into the statistical methods and practical techniques to deal with common modalities of data - image, text and graphs. Specific programming frameworks required for data science will be covered in the lab sessions.

Advanced course in reinforcement learning. Topics: Policy gradient methods, gradient estimation, analysis of valued-based function approximation methods, optimal control and automatic differentiation, bilevel optimization in meta-learning and inverse reinforcement learning.

This is a course on representation learning in general and deep learning in particular. Deep learning has recently been responsible for a large number of impressive empirical gains across a wide array of applications including most dramatically in object recognition and detection in images, natural language processing and speech recognition. 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. We will also review recent work on attention mechanism and efforts to incorporate memory structures into neural network models. We will also consider some of the modern neural network-base generative models such as Generative Adversarial Networks and Variational Autoencoders.