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Course Content
Module 1: Applied Machine Learning
Supervised Learning
Supervised Learning
- Regression Maximum Likelihood, Least-Squares, Regularization
- Bayesian Methods Bayes Rule, MAP Inference, Active Learning
- Foundation Classification Algorithms Nearest Neighbors, Perception, Logistic Regression
- Refinements to Classification Kernel Methods, Gaussian Process
- Intermediate Classification Algorithms SVM, Trees, Forests and Boosting
- Unsupervised Learning
- Clustering Methods K-Means Clustering, E-M, Gaussian Mixtures
- Recommendation Systems Collaborative Filtering, Topic Modeling, PCA
- Sequential Data Models Markov and Hidden Markov Models, Kalman Filters
- Association Analysis
- Clustering Methods – II Model Comparisons, Analysis Considerations
Module 2: Applied Artificial Intelligence
- Introduction to Artificial Intelligence
- Intelligent Agents and Uninformed Search
- Heuristic Search
- Adversarial Search and Games
- Constraint Satisfaction Problems
- Reinforcement Learning
Logical Agents
- AI applications: Natural Language Processing
- AI Applications and Course Review
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