Course details

Research Topics in ML and DL

Research Topics in ML and DL


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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



Expected Duration (hours)
0.7

Lesson 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
  • Course Number:
    it_mlrtmddj_01_enus

    Expertise Level
    Intermediate