2024
Robotics Sensing and Estimation
Covers mathematical fundamentals of Bayesian filtering applied to sensing and estimation in mobile robotics. Topics include MLE, EM, Gaussian and particle filters, SLAM, visual features, optical flow, and HMMs
Intelligent Systems
Introduces fundamental concepts in machine perception, including edge detection, segmentation, texture analysis, image registration, and compression
Intro to Autonomous Vehicles
Introduces fundamentals of autonomous vehicles through robotics "cooperation", focusing on fast prototyping, project management, programming, computer vision, ROS, and deep learning. Students build scale cars in small teams using best engineering practices
Machine Learning
Introduction to pattern recognition and machine learning, covering decision functions, statistical classifiers, generative vs. discriminant methods, feature selection, regression, unsupervised learning, clustering, and applications
Statistical Learning
Covers Bayesian decision theory, parameter estimation, bias-variance trade-off, Bayesian estimation, predictive distribution, priors, dimensionality reduction, PCA, Fisher's LDA, density estimation, EM, and applications
Probabilistical Graphical Models
Explores Bayes’ rule for probabilistic reasoning, graphical models for knowledge encoding, conditional independence, D-Separation, Markov random fields, inference methods, MCMC sampling, sequential data analysis, and algorithms like Viterbi, BCJR, and Baum-Welch for parameter estimation
Deep Learning and Applications
Covers deep learning fundamentals, neural network architectures (ConvNet, RNN), optimization algorithms, and applications in computer vision, robotics, and NLP. Includes hands-on PyTorch implementation
Stochastic Processes and Dynamic Systems
Covers diffusion equations, estimation and detection (linear and nonlinear), random fields, stochastic dynamic system optimization, and applications of stochastic optimization
Random Processes
Explores random variables, probability distributions, stochastic processes, stationarity, power spectrum, and spectral density. Includes stochastic integrals, spectral representation of WSS processes, harmonizable processes, and moving average representations
Probability and Random Processes
Covers random processes, stationary processes (correlation, power spectral density), Gaussian processes and their linear transformations, point processes, and random noise in linear systems
Stochastic Processes I
Covers random vectors, multivariate densities, covariance matrix, multivariate normal distribution, random walk, and Poisson process
Convex Optimizations and Applications
Covers convex optimization theory and algorithms, focusing on problem formulation, duality, and applications in system design, pattern recognition, combinatorial optimization, and financial engineering
Numerical Linear Analysis
Covers numerical methods for linear algebraic systems and least squares problems, including orthogonalization, ill-conditioned problems, and eigenvalue and singular value computations
Linear Algebra and Applications
Builds mathematical foundations of linear algebra for applications in signal processing, communication, and machine learning. Topics include vector and Hilbert spaces, orthogonal projection, sparsity, eigenanalysis, Hermitian and positive semidefinite matrices, SVD, and PCA
2023
Software Foundations I
Software analysis, design, and development using C++, focusing on data structures, algorithms, object-oriented methods, and design patterns. Includes real-world challenges in building, testing, and debugging software to develop a strong foundation in modern software design and architecture
Software Foundations II
Builds upon the C++ foundation of ECE 141A to design and implement a database management solution. Covers STL, design patterns, parsing, searching, sorting, algorithmic thinking, design partitioning, and best practices in debugging and testing
The Art of Product Engineering I
Builds on electrical and computer engineering fundamentals to develop skills in software, full-stack engineering, and commercial product development. Focuses on researching, designing, and creating an IoT device for an emerging market. Emphasis on fullstack development and entrepreneurship
The Art of Product Engineering II
Second course for The Art of Product Engineering I with an emphasis on IoT development and continuation of entrepreneurship and market fit. Topics include MQTT and embedded development with ESP32
Linear Electronic Systems
Linear active circuit and system design covering frequency response, Laplace transforms, and filter design with operational amplifiers. Combines lecture and lab for analysis, design, simulation, and testing of circuits and systems
Linear Control System Theory II
Time-domain state-variable formulation for discrete-time and continuous-time linear systems, including state-space realizations from transfer functions. Covers stability, controllability, observability, minimal realizations, and pole placement via full-state feedback
2022
Linear Systems Fundamentals
Complex variables, singularities, and residues applied to signal and system analysis in continuous and discrete time; includes Fourier, Laplace, and z-transforms, LTI systems, impulse and frequency response, transfer functions, poles and zeros, stability, convolution, sampling, and aliasing
Linear Control System Theory I
Stability of continuous- and discrete-time SISO LTI control systems using frequency domain methods; includes transient and steady-state behavior, root locus, Bode, Nyquist, Nichols plots, and compensator design
Introduction to Linear and Nonlinear Optimizations
Linear least squares problems, constrained and unconstrained quadratic optimization, and geometry of linear transformations; introduces nonlinear optimization with applications in signal processing, system identification, robotics, and circuit design