Fall 2015 Machine Learning
Fall 2015 Machine Learning
- 1.1 Machine Learning
- Types of Machine Learning
- Model Selection in Machine Learning
- 2 Probability and Naive Bayes
- 3 Decision Trees
- 4 Logistic Regression
- 5 Perceptron
- Neural Networks
- 7 Learning Objective Functions
- Back Propagation
- 9 Support Vector Machines
- 10 Dual SVM and Kernels
- 11 Feature Maps and Kernels
- 12 SMO and Stochastic SVM
- 13 Regression
- 14 Principal Component Analysis
- 15 Clustering and Mixture Models
- 16 Variational EM and K Means
- 15.1 GMM Degeneracy
- 17 Probabilistic Graphical Models and Bayesian Networks
- Markov Models
- Hidden Markov Models
- Undirected Graphical Models
- Belief Propagation
- Graphical Models Wrap up
- What is Learning Theory?
- Model Complexity and VC Dimension
- Fairness in Machine Learning
- The Queen is King | Magnus Carlsen vs Fabiano Caruana | Norway Chess 2018
CS4804 Intro to AI, Fall 2016
CS4804 Intro to AI, Fall 2016
- Intelligent Agents
- Intro to Search
- Informed Search
- A* Optimality
- Pruning (Alpha-Beta)
- Probability Basics
- More Realistic Adversarial Settings
- Markov Decision Processes
- Passive Reinforcement Learning
- Active Reinforcement Learning
- Logic
- First Order Logic Overview
- Bayesian Networks
- Markov Models
- Hidden Markov Models
- Particle Filters
- Types of Machine Learning
- Model Selection in Machine Learning
- Neural Networks
- Back Propagation