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