Teaching Assistant

Remote

Online Master of Engineering (MEng) in Computer Engineering Course Teaching Assistants (TAs) will support faculty and students during course run in areas including facilitation of live sessions, holding office hours, monitoring discussion forums, assisting with administration of the Coursera platform, grading of assignments, and providing technical support related to course content for students when necessary. TAs should have demonstrated proficiency in Computer Engineering course content at or above the graduate level.

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The Thayer School of Engineering at Dartmouth College is seeking teaching assistants (TAs) for courses in the new online Master of Engineering (MEng) in Computer Engineering. Course TAs will support course faculty and students during the course run. Currently we are looking for TAs for Machine Learning with Prof Peter Chin during Spring Term 2024 and for Signal Processing with Prof Kelly Seals during Spring Term 2024. TA responsibilities will begin in early March and end in mid-June. Spring term courses will be in session from March 25 to June 4, 2024.

TAs should have demonstrated proficiency in the content of the relevant course at the graduate level. The courses are described below:

The Machine Learning course will start with requisite mathematical backgrounds (probability theory, statistics, some basic linear algebra, etc). The course will then cover unsupervised ML models, namely linear regression/classification models, neural network models, and kernel machine models. The course will end with unsupervised learning and discuss unsupervised ML algorithms, such as graphical models K-clustering algorithm, EM (Expectation Maximization) algorithm, autoencoders, PCA/ ICA, etc. Programming using Python and ML software packages (PyTorch, Tensorflow, etc.) will be used to supplement understanding of the mathematics and algorithms covered in this course and to develop large-scale applications of ML algorithms.

In Signal Processing, the mathematical theories that underpin the discipline of signal processing are presented and used in applied settings, allowing students to analyze, optimize, and adjust a wide range of data and signals. Students will learn topics such as sampling, signal filtering, noise reduction, data compression, the discrete Fourier transform (and fast Fourier transform), Fourier analysis, and feature extraction. Modeling a random signal as a stochastic process is used to investigate the analysis and processing of signals from a statistical viewpoint.

Lectures for both courses are offered asynchronously, with weekly live sessions that are optional for students. TAs will be expected to attend and possibly facilitate portions of the weekly live sessions. The expected TA time commitment is approximately 15 hours per week. The online MEng courses will be offered on the Coursera platform. There will be required training in March before the term begins.

Brief description of the MEng in Computer Engineering degree program: Intelligent systems are machines that interact with the world via a combination of sensing, computing, and actuation. In this degree program, students will learn to engineer the sensing and computing components of intelligent systems. The skills that students will master are essential for the fields of virtual/augmented reality, autonomous robots, self-driving cars, AI virtual assistants, wearable/implantable devices, and more.

There are nine required courses in the full program: