1. What is Artificial Intelligence?
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2. AI Terminology
Supervised Learning: Partitioning data based on labels (input/output pairs)
Unsupervised Learning: Partitioning data based on data structure (input only)
Reinforcement Learning: Supervised or unsupervised data partitioning based on a reward feedback from environment (input and/or output with reward)

3. AI terminology SUPERVISED Learning
Decision Trees: model of decisions and their possible consequences
Naïve Bays Classifier: probabilistic classifier with assumptions between features consequences
Least Squares Regression: fitting straight line with minimum distance
SUPERVISED Learning
Logistic Regression: fitting binomial data using logistic distribution
Support Vector Machine: linear hyperplane to separate groups
Ensemble Method: weighted vote of prediction from set of classifiers

4. UNSUPERVISED Learning
AI terminology (cont.)
Clustering Algorithms: grouping similar objects
Principal Component Analysis (PCA): orthogonal transformation to convert a set of possibly correlated variables into linear set of uncorrelated variables
UNSUPERVISED Learning
Examples:
Centroid-based algorithms
Connectivity-based algorithms
Density-based algorithms
Probabilistic
Dimensionality Reduction
Neural networks / Deep Learning
Independent Component Analysis: separating multivariate signals into statistically independent/non-Gaussian subcomponents

5. REINFORCEMENT Learning
AI terminology (cont.)
SARSA: State, action, reward, state, action. Reward based Markov decision process policy
Monte Carlo: learns from episodes of experience/policies
Q-learning: finds optimal policy to maximize reward
REINFORCEMENT Learning