Machine Learning
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22: Function Approximation in Reinforcement Learning
Course content
1: Introduction to Machine Learning
2: Supervised Learning
3: Data & Features
4: Vectors
5: K-Nearest Neighbors (KNN) Algorithm
6: Linear Regression - Least Squares Method
7: Linear Regression - Gradient Descent
8: Logistic Regression
9: Support Vector Machine
10: Introduction to Unsupervised Learning and Clustering
11: Hierarchical Clustering
12: K-Means Clustering
13: Probability Theory
14: Naive Bayes Learning Algorithm
15: Bayes Nets
16: Introduction to Decision Trees
17: Entropy and Information Gain
18: Overfitting and Pruning
19: Advantages and Limitations of Decision Trees
20: Introduction to Reinforcement Learning
21: Reinforcement Learning through Feedback Networks
22: Function Approximation in Reinforcement Learning
23: Introduction to Ensemble Methods
24: Random Forests
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Function Approximation in Reinforcement Learning
Content for Monday, September 23, 2024
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21: Reinforcement Learning through Feedback Networks
23: Introduction to Ensemble Methods