ICML 2011
TechTalks from event: ICML 2011
Tutorial: Machine Learning in Ecological Science and Environmental Policy
-
Machine Learning in Ecological Science and Environmental Policy: Part 1This tutorial will review opportunities for machine learning research in ecological science and environmental policy. We will present examples of existing and emerging applications of machine learning and challenges for machine learning research. -
Machine Learning in Ecological Science and Environmental Policy: Part 2Continuation of first half of the tutorial.
- All Sessions
- Keynotes
- Bandits and Online Learning
- Structured Output
- Reinforcement Learning
- Graphical Models and Optimization
- Recommendation and Matrix Factorization
- Neural Networks and Statistical Methods
- Latent-Variable Models
- Large-Scale Learning
- Learning Theory
- Feature Selection, Dimensionality Reduction
- Invited Cross-Conference Track
- Neural Networks and Deep Learning
- Latent-Variable Models
- Active and Online Learning
- Ensemble Methods
- Tutorial: Introduction to Bandits: Algorithms and Theory
- Tutorial: Machine Learning for Large Scale Recommender Systems
- Tutorial: Learning Kernels
- Tutorial : Collective Intelligence and Machine Learning
- Tutorial: Machine Learning in Ecological Science and Environmental Policy
- Tutorial: Machine Learning and Robotics
- Test-of-Time
- Best Paper
- Robotics and Reinforcement Learning
- Transfer Learning
- Kernel Methods
- Optimization
- Learning Theory
- Invited Cross-Conference Session
- Neural Networks and Deep Learning
- Reinforcement Learning
- Bayesian Inference and Probabilistic Models
- Supervised Learning
- Social Networks
- Evaluation Metrics
- statistical relational learning
- Outlier Detection
- Time Series
- Graphical Models and Bayesian Inference
- Sparsity and Compressed Sensing
- Clustering
- Game Theory and Planning and Control
- Semi-Supervised Learning
- Kernel Methods and Optimization
- Neural Networks and NLP
- Probabilistic Models & MCMC
- Online Learning
- Ranking and Information Retrieval