Basic understanding of computational principles and information coding in biological brains; biologically plausible neural and synaptic models; neutrally inspired network architectures, learning mechanisms, and trainingalgorithms; hardware architectures and circuits for supporting efficient neurally inspired learning.

No Prerequisites

4

Units

Letter

Grading

1, 2, 3

Passtime

Graduate students only

Level Limit

Engineering

College
LI P
No info found

Lecture

PHELP1431
M W
12:00 PM - 13:50 PM
30 / 30 Closed
ECE 254B
29 / 30 Enrolled
Advanced Computer Architecture: Parallel Processing
Behrooz Parhami 2.4
M W
10:00 AM - 11:50 AM
50.9% A
ECE 256C
17 / 25 Enrolled
Advanced VLSI Architecture and Design
Forrest Brewer 3.2
T R
17:00 PM - 18:15 PM
75.5% A
ECE 271A
25 / 30 Closed
Principles of Optimization
Mostofi Y C
M W
12:00 PM - 13:50 PM
92.9% A
ECE 272B
30 / 30 Full
Artificial Intelligence in Design and Test Automation
Li-C Wang 4.3
M W
17:00 PM - 18:15 PM
93.2% A
ECE 277
30 / 30 Closed
Pattern Recognition
Kenneth Rose 4.4
T R
10:00 AM - 11:50 AM
76.4% A
ECE 295
4 / 30 Enrolled
Group Studies: Controls, Dynamical Systems, and Computation
Andrew Teel 4.3
F
14:30 PM - 16:50 PM