This 4-day class on advanced machine learning includes an in-depth coverage of new and advanced methods in machine learning, as well as their underlying theory. It emphasizes approaches with practical relevance and discusses several recent applications of machine learning in areas like information retrieval, recommender systems, data mining, computer vision, natural language processing and robotics.
Audience
Intermediate level data analysts interested in expanding their data mining processes. We emphasize Data Foundation and Machine Learning concepts.
Prerequisites
This machine learning course is for individuals with intermediate data analysis skills and basic knowledge of descriptive statistics.
Course Outline
1. Supervised Batch Learning
- Model
- Decision Theoretic Foundation
- Model Selection
- Model Assessment
- Empirical Risk Minimization
2. Decision Trees
- TDIDT
- Attribute Selection
- Pruning and Overfitting
3. Statistical Learning Theory
- Generalization Error Bounds
- VC Dimension
4. Large-Margin Methods and Kernels
- Linear Rules
- Margin
- Perceptron
- SVMs
- Duality
- Non-Linear Rules Through Kernels
5. Deep Learning
- Multi-Layer Perceptrons
- Deep Networks
- Stochastic Gradient
- Computer Vision
6. Large Language Models
- Network Architectures
- Fine Tuning
- Preference
- Learning from Human Feedback
7. Probabilistic Models
- Generative vs. Discriminative
- Maximum Likelihood
- Bayesian Inference
8. Structured Output Prediction
- Undirected Graphical Models
- Structural SVMs
- Conditional Random Fields
9. Latent Variable Models
- K-Means Clustering
- Mixture of Gaussians
- Expectation-Maximization Algorithms
- Matrix Factorization
- Embeddings
10. Online Learning
- Experts
- Bandits
- Online Convex Optimization
11. Causal Inference
- Interventional vs. Observational Data
- Treatment Effect Estimation