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Introduction

Describing the Structure of Unlabled Data

• Unsupervised Machine Learning

Recognizing, Clustering and Generating Images, Video Sequences and Motion-capture Data

• Deep Belief Networks (DBNs)

Reconstructing the Original Input Data from a Corrupted (Noisy) Version

• Feature Selection and Extraction
• Stacked Denoising Auto-encoders

Analyzing Visual Images

• Convolutional Neural Networks

Gaining a Better Understanding of the Structure of Data

• Semi-Supervised Learning

Understanding Text Data

• Text Feature Extraction

Building Highly Accurate Predictive Models

• Improving Machine Learning Results
• Ensemble Methods

Summary and Conclusion