ECE 59500 - Machine Learning II
Course Details
Lecture Hours: 3 Credits: 3
This is an experiential learning course.
Counts as:
- EE Elective
- CMPE Selective - Special Content
Experimental Course Offered:
Fall 2019
Requisites:
ECE 30200 or equivalent and MA 26500 or equivalent
Catalog Description:
Data-driven machine learning, Probabilistic inference using graphical models, sampling methods, historical perspective for deep learning algorithms and connection to nervous activity, learning with Boltzmann machines, cross-correlation and backpropagation, programming environments, tuning network parameters, generative networks, security aspects of machine learning, reinforcement learning, transfer learning, learning with explicit memory.
Required Text(s):
None.
Recommended Text(s):
- Deep Learning , 1st Edition , Ian Goodfellow, Yoshua Bengio, and Aaron Courville , MIT Press , 2016 , ISBN No. 0262035618
Engineering Design Content:
- Establishment of Objectives and Criteria
- Synthesis
- Analysis
- Construction
- Testing
- Evaluation
Engineering Design Consideration(s):
- Economic