A laboratory-intensive survey of analog and digital electronics, intended to teach students of physics or related fields enough electronics to be effective in experimental research and to be comfortable learning additional topics from reference textbooks. Analog topics include voltage dividers, impedance, filters, operational amplifier circuits, and transistor circuits. Digital topics may include logic gates, finite-state machines, programmable logic devices, digital-to-analog and analog-to-digital conversion, and microcomputer concepts. Recommended for students planning to do experimental work in physical science.

First term course in intermediate electromagnetism. Topics include electrostatics, static potential theory, multipole expansions, Laplace equation, image solutions, fields in polarized matter.

This is a course on the fundamentals of data analysis and statistical inference for the natural sciences. Topics include probability distributions, linear and non-linear regression, Monte Carlo methods, frequentist and Bayesian data analysis, parameter and error estimation, Fourier analysis, power spectra, and signal and image analysis techniques. Students will obtain both the theoretical background in data analysis and also get hands-on experience analyzing real scientific data. Prerequisite: Prior programming experience.

This course covers the fundamentals of atmosphere and ocean dynamics, and aims to put these in the context of climate change in the 21st century. Large-scale atmospheric and oceanic circulation, the global energy balance, and the global energy balance, and the global hydrological cycle. We will introduce concepts of fluid dynamics and we will apply these to the vertical and horizontal motions in the atmosphere and ocean. Concepts covered include: hydrostatic law, buoyancy and convection, basic equations of fluid motions, Hadley and Ferrel cells in the atmosphere, thermohaline circulation, Sverdrup ocean flow, modes of climate variability (El-Nino, North Atlantic Oscillation, Southern Annular Mode). The course will incorporate student led discussions based on readings of the 2007 Intergovernmental Panel on Climate Change (IPCC) report and recent literature on climate change. Aimed at undergraduate or graduate students who have no prior knowledge of meteorology or oceanography or training in fluid mechanics. Previous background in calculus and/or introductory physics is helpful. This is a general course which spans many subdisciplines (fluid mechanics, atmospheric science, oceanography, hydrology).

This course covers the fundamentals of atmosphere and ocean dynamics, and aims to put these in the context of climate change in the 21st century. Large-scale atmospheric and oceanic circulation, the global energy balance, and the global energy balance, and the global hydrological cycle. We will introduce concepts of fluid dynamics and we will apply these to the vertical and horizontal motions in the atmosphere and ocean. Concepts covered include: hydrostatic law, buoyancy and convection, basic equations of fluid motions, Hadley and Ferrel cells in the atmosphere, thermohaline circulation, Sverdrup ocean flow, modes of climate variability (El-Nino, North Atlantic Oscillation, Southern Annular Mode). The course will incorporate student led discussions based on readings of the 2007 Intergovernmental Panel on Climate Change (IPCC) report and recent literature on climate change. Aimed at undergraduate or graduate students who have no prior knowledge of meteorology or oceanography or training in fluid mechanics. Previous background in calculus and/or introductory physics is helpful. This is a general course which spans many subdisciplines (fluid mechanics, atmospheric science, oceanography, hydrology).

Classic case studies of successful reductionistic models of complex phenomena, emphasizing the key steps of making estimates, using them to figure out which physical variables and phenomena will be most relevant to a given system, finding analogies to purely physical systems whose behavior is already known, and embodying those in a mathematical model, which is often implemented in computer code. Topics may include bacterial genetics, genetic switches and oscillators; systems that sense or utilize light; superresolution and other newmicroscopy methods; and vision and other modes of sensory transduction.

This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.

This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.

This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.

This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.