List of courses

EE1G1 Introduction to Electrical Engineering (5 EC)

Introduction to Electrical Engineering (EE) and the EE BSc programme at TU Delft

EE1L11 EPO-1: Booming Bass (5 EC)

(not running) Build, analyze and characterize a sound system consisting of a power source, amplifier and 3-way filters

EE2L1 Integrated Project 3 (5 EC)

BSc 2nd year practical

EE2L21 EPO-4: BSc 2nd year practical (5 EC)

(not running) Two variants: "KITT: Autonomous driving" and "Biosensing"

EE2ML1 Introduction to Machine Learning (3 EC)

(not running) Elective course for BSc-EE students

EE2S1 Signals and systems (5 EC)

Theory of signal transformations and LTI systems

EE2S31 Signal processing (5 EC)

(not running) Digital signal processing; stochastic processes

EE2T1 Telecommunication and Sensing (5 EC)

A first course in telecommunication and sensing systems

EE2T11 Telecommunications A (5 EC)

(not running) A first course in telecommunication, with focus on the physical layer. Includes a lab course related to Signals and Systems (preparing for EPO-4)

EE2T21 Telecommunications B (5 EC)

(not running) Modulation, introduction to networks

EE3115TU Digital Communication Systems (3 EC)

Transmission of digital signals

EE3350TU Introduction to Radio Astronomy (3 EC)

(not running) Introduction to the science and technology of radio astronomy

EE4182 Digital audio and speech processing (6 EC)

(not running) Audio, speech and acoustic signal processing, speech enhancement, microphone-array signal processing

EE4530 Applied convex optimization (5 EC)

Applied convex optimization: role of convexity in optimization, convex sets and functions, Canonical convex problems (SDP, LP, QP), second-order methods, first-order methods for large-scale problems.

EE4540 Distributed signal processing (5 EC)

Signal processing techniques for decentralized signal processing

EE4610 Digital IC design (3 EC)

Analysis and design of digital systems with full comprehension of its performance, power dissipation, size and reliability.

EE4685 Machine learning, a Bayesian perspective (5 EC)

Mathematical foundation for machine learning algorithms, presented from a statistical (Bayesian) and optimization point of view.

EE4715 Array processing (5 EC)

Array processing techniques for signal separation and parameter estimation

EE4740 Data compression: Entropy and sparsity perspectives (5 EC)

Data compression and its connections to information theory and compressed sensing

EE4750 Tensor networks for green AI and signal processing (4 EC)

Introduction to multilinear algebra, tensor decompositions, and their applications for green AI and biomedical signal processing

EE4760 Probablistic sensor fusion (3 EC)

EE4C03 Statistical digital signal processing (5 EC)

A second course on digital signal processing: random signals, covariances, linear prediction, spectrum estimation, optimal filtering, Wiener and Kalman filters, LMS and RLS algorithm

EE4C11 Systems engineering (5 EC)

Introduction to systems engineering processes

EE4C12 Machine learning for Electrical Engineering (5 EC)

Introduction at MSc level

EEX01 Introduction to Machine Learning (5 EC)

ET-Mi-201 Electronics for Robotics (30 EC)

Minor

ET4358 Fundamentals of wireless communications (5 EC)

Overview of the essential aspects of the physical layer as well as transmission system design aspects of generic wireless communications systems

ET4386 Estimation and detection (5 EC)

Basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, localization, system identification, and elsewhere.