Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.
Requirements
Good understanding of Machine Learning. At least theoretical knowledge of Deep Learning.
Course Outline
- Machine Learning Limitations
- Machine Learning, Non-linear mappings
- Neural Networks
- Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent
- Back Propagation
- Deep Sparse Coding
- Sparse Autoencoders (SAE)
- Convolutional Neural Networks (CNNs)
- Successes: Descriptor Matching
- Stereo-based Obstacle
- Avoidance for Robotics
- Pooling and invariance
- Visualization/Deconvolutional Networks
- Recurrent Neural Networks (RNNs) and their optimizaiton
- Applications to NLP
- RNNs continued,
- Hessian-Free Optimization
- Language analysis: word/sentence vectors, parsing, sentiment analysis, etc.
- Probabilistic Graphical Models
- Hopfield Nets, Boltzmann machines
- Deep Belief Nets, Stacked RBMs
- Applications to NLP, Pose and Activity Recognition in Videos
- Recent Advances
- Large-Scale Learning
- Neural Turing Machines