Besides the basic/foundation classes, here are some of the more interesting classes I took:
CMPE 458 Programming language processors
Introduction to the systematic construction of a compiler: grammars and languages, scanners, top-down and bottom-up parsing, runtime organization, symbol tables, internal representations; Polish notation, syntax trees, semantic routines, storage allocation, code generation, interpreters.
ELEC 472 Artificial Intelligence
Fundamental concepts and applications of intelligent and interactive system design and implementation. Topics include: problem formulation and experiment design, search techniques and complexity, decision making and reasoning, data acquisition, data pre-processing (de-noising, missing data, source separation, feature extraction, feature selection, dimensionality reduction), supervised learning, unsupervised learning, and swarm intelligence.
CMPE 452 Neural and Genetic computing
Introduction to neural and genetic computing. Topics include associative memory systems, neural optimization strategies, supervised and unsupervised classification networks, genetic algorithms, genetic and evolutionary programming. Applications are examined, and the relation to biologic systems is discussed.
ELEC 473 Cryptography & NetSec
Cryptography topics include: block ciphers, advanced encryption standard, public key encryption, hash functions, message authentication codes, digital signatures, key management and distribution, and public-key infrastructure. Network security topics include: user authentication, network access control, Kerberos protocol, transport layer security (TLS), IP security (IPSec), electronic mail security, and wireless network security.
ELEC 475 Comp Vision with Deep Learning
Deep learning methods are highly effective at solving many problems in computer vision. This course serves as an introduction to these two areas and covers both the theoretical and practical aspects required to build effective deep learning-based computer vision applications. Topics include classification, convolutional neural networks, object detection, encoder-decoders, segmentation, keypoint and pose estimation, generative adversarial networks, and transformers. Labs and assignments will emphasize practical implementations of deep learning systems applied to computer vision problems.
CMPE 332 Database Management Systems
Data models: relational, entity-relationship. Relational query languages: relational algebra and SQL. Relational database design. Application interfaces and embedded SQL. Storage and indexing.
