• Online, Self-Paced
Course Description

Explore research topics in Machine Learning and Deep Learning and the topics being addressed in these fields.

Learning Objectives

Research Topics in ML and DL

  • Course Overview
  • understand the efforts being undertaken to reduce overfitting using the dropout technique
  • understand leading edge multi-label learning algorithms
  • understand the proposed learning framework for deep residual learning that improves training of networks that are significantly deeper than traditional neural networks
  • understand how initializing a network with transferred features may boost generalization performance
  • understand how convolutional neural networks may be utilized as a powerful class of models for image recognition
  • understand the dataset that advances state-of-the-art object recognition by considering the context within the question of scene understanding
  • understand the proposed framework for estimating generative models via an adversarial process that successfully estimates the probability that a sample came from training data rather than a generative model
  • understand how optimal nearest neighbor algorithms perform compared to traditional nearest neighbor algorithms
  • understand how an ensemble of regression trees may successfully estimate facial landmark positions while delivering real-time performance and high quality predictions
  • understand how a proposed new scene-centric database is successfully used for learning deep features for scene recognition
  • recognize how ELM tends to produce better scalability, generalization performance, and faster learning than traditional support vector machine
  • understand the trending research topics in ML and DL

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Technology R&D