Credits: 1
Duration: 15 Weeks
Dates: Thu
Credits: 1
Duration: 15 Weeks
Dates: Thu,Tue
Hands-on lab accompanying Digital Electronics. Lab sessions will contain hands-on experience with logic circuits & microcontrollers. The course culminates with a student developed final project.
Music Technology (Undergraduate)
1 credits – 15 Weeks
Credits: 3
Duration: 15 Weeks
Dates: Tue
An introduction to Digital Electronics, including binary systems & logic. Students must enroll in a Lab section to apply hands-on experience in simple computer programming techniques, digital processing applied to music with specific relevance to computer music synthesis & MIDI.
Music Technology (Undergraduate)
3 credits – 15 Weeks
Credits: 1
Duration: 15 Weeks
Dates: Tue
Credits: 1
Duration: 15 Weeks
Dates: Tue,Thu
Credits: 1
Duration: 15 Weeks
Dates: Tue,Thu,Mon
Credits: 1
Duration: 15 Weeks
Dates: Tue,Thu,Mon
Credits: 1
Duration: 15 Weeks
Dates: Tue,Thu,Mon,Fri
Credits: 1
Duration: 15 Weeks
Dates: Tue,Thu,Mon,Fri
Hands-on lab accompanying Analog Electronics. Lab sessions will contain hands-on experience with analog audio circuitry. The course culminates with a student developed final project.
Music Technology (Undergraduate)
1 credits – 15 Weeks
Credits: 3
Duration: 15 Weeks
Dates: Fri
Credits: 3
Duration: 15 Weeks
Dates: Fri
This course focuses on electronic music synthesizer techniques. Concepts in the synthesis of music, including generation of sound, voltage control, and treatment of sound and tape techniques. Included is a short synopsis of the history and literature of analog electronic music. Students complete laboratory tasks and compositions on vintage synthesizer modules and create one or more final projects that demonstrate(s) the application of these concepts.
Music Technology (Undergraduate)
3 credits – 15 Weeks
Credits: 3
Duration: 15 Weeks
Dates: Wed
Credits: 3
Duration: 15 Weeks
Dates: Wed
Credits: 3
Duration: 15 Weeks
Dates: Wed
This course is an introduction to the field of machine learning, covering fundamental techniques for classification, regression, dimensionality reduction, clustering, and model selection. A broad range of algorithms will be covered, such as linear and logistic regression, neural networks, deep learning, support vector machines, tree-based methods, expectation maximization, and principal components analysis. The course will include hands-on exercises with real data from different application areas (e.g. text, audio, images). Students will learn to train and validate machine learning models and analyze their performance. | Knowledge of undergraduate level probability and statistics, linear algebra, and multi-variable calculus. Prerequisite: Graduate standing.
Computer Science (Graduate)
3 credits – 15 Weeks
Credits: 3
Duration: 15 Weeks
Dates: Thu
An important goal of artificial intelligence (AI) is to equip computers with the capability of interpreting visual inputs. Computer vision is an area in AI that deals with the construction of explicit, meaningful descriptions of physical objects from images. It includes as parts many techniques from image processing, pattern recognition, geometric modeling, and cognitive processing. This course introduces students to the fundamental concepts and techniques in computer vision. | Knowledge of Data Structures and Algorithms, proficiency in programming, and familiarity with matrix arithmetic. Prerequisites: Graduate standing.
Computer Science (Graduate)
3 credits – 15 Weeks
Credits: 3
Duration: 15 Weeks
Dates: Mon
Credits: 3
Duration: 15 Weeks
Dates: Mon,Sat
Credits: 3
Duration: 15 Weeks
Dates: Mon,Sat
Credits: 3
Duration: 15 Weeks
Dates: Mon,Sat,Wed
This course takes a top-down approach to computer networking. After an overview of computer networks and the Internet, the course covers the application layer, transport layer, network layer and link layers. Topics at the application layer include client-server architectures, P2P architectures, DNS and HTTP and Web applications. Topics at the transport layer include multiplexing, connectionless transport and UDP, principles or reliable data transfer, connection-oriented transport and TCP and TCP congestion control. Topics at the network layer include forwarding, router architecture, the IP protocol and routing protocols including OSPF and BGP. Topics at the link layer include multiple-access protocols, ALOHA, CSMA/CD, Ethernet, CSMA/CA, wireless 802.11 networks and linklayer switches. The course includes simple quantitative delay and throughput modeling, socket programming and network application development and Ethereal labs. | Knowledge of Python and/or C. Prerequisite: Graduate standing.
Computer Science (Graduate)
3 credits – 15 Weeks