ECE 59500 - Machine Learning II

Course Details

Lecture Hours: 3 Credits: 3

This is an experiential learning course.

Counts as:

  • CMPE Special Content Elective
  • EE Elective

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):

  1. 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