E236: Making Sense of Data: Introduction to Statistical Inference

We are often presented with a set of data, often very large, about various processes or phenomena (health data, economic performance, environmental indices, experimental observations). How do we make sense of them? We will describe various statistical tools that will allow to draw meaningful conclusions (inference). Topics covered include various distribution functions and criteria for…

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E238E: Robust Optimization and Applications

Robust optimization is concerned with decision-making under uncertainty, where the emphasis is on guaranteeing a maximal level of performance despite unknown-but-bounded uncertainty. This course covers the essentials of robust optimization and applications to various areas of engineering and machine learning. Students will learn the basic techniques and be exposed to various ways one can model…

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E236A: Applied Data Science for Engineers

This mini-course aims at providing basics of Data Science to students and professionals who need to work with and analyze a large volume of data. The base programming language is Matlab, but techniques taught, and topics covered can be coded in any programming language. The course is aimed at students and audience in engineering and…

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E238B: Optimization Theory and Practice

Optimization theory concerns the selection of a best option from a set of available options. Formulating an optimization problem involves describing the feasible set as well as prescribing a notion of “best”. This setup, although simple, is one of the most important and widespread ideas in engineering and the sciences. This course will begin by…

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E235A: Python for Engineers

In recent years Python has emerged as an indispensable programming language for engineers, both practicing and academic, as well as data scientists, web developers, and many others. However the language is vast and includes many features that are not immediately relevant to most engineers. The goal of this course is to help students to quickly…

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E237B: Hands-on Deep Learning

Deep learning has become an unprecedented and universally powerful tool with applications to a plethora of application domains with problems ranging from vision, speech, natural language processing, robotics, and several other areas. This course will provide select important advances in deep learning. We will particularly focus on a hands-on approach where students will learn techniques…

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E238D: Optimization, Control and Artificial Intelligence for Smart Grids and Energy Systems

Power systems around the world are being modernized to address environmental concerns, reduce costs, and guarantee access to electricity all the time. Four main criteria for this upgrade are efficiency, reliability, resiliency, and sustainability. Recent advances in various technologies are the key enablers for this modernization. Nevertheless, such physical systems are becoming overwhelmingly large-scale and…

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E236B: Data Science and Machine Learning Fundamentals

The Data Science and Machine Learning Fundamentals course provides an introduction to machine learning in the context of data science. By the end of the course, students will know how to clean, visualize, and model real world datasets using basic machine learning techniques. The course assumes a familiarity with the Python programming language.

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