Course Outline
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
- Python programming experience
- An understanding of basic principles of machine learning
21 hours (usually 3 days including breaks)
