An Exhaustive List of Topics in Machine Learning

I. Introduction

• Definition of machine learning

• Types of machine learning (supervised, unsupervised, reinforcement)

• Applications of machine learning

II. Supervised Learning A. Basics of Supervised Learning

1. Types of problems (classification, regression)

2. Learning process (training, testing)

3. Evaluation metrics (accuracy, precision, recall, F1 score)

4. Bias-variance tradeoff B. Linear Models

5. Linear Regression

6. Logistic Regression

7. Naive Bayes Classifier

8. Support Vector Machines C. Tree-Based Models

9. Decision Trees

10. Random Forests

11. Gradient Boosted Trees D. Instance-Based Models

12. k-Nearest Neighbors E. Deep Learning Models

13. Artificial Neural Networks

14. Convolutional Neural Networks

15. Recurrent Neural Networks

III. Unsupervised Learning A. Basics of Unsupervised Learning

1. Types of problems (clustering, dimensionality reduction)

2. Learning process (training, testing)

3. Evaluation metrics (silhouette score, reconstruction error) B. Clustering Algorithms

4. k-Means Clustering

5. Hierarchical Clustering C. Dimensionality Reduction Algorithms

6. Principal Component Analysis

7. t-Distributed Stochastic Neighbor Embedding

8. Autoencoders

IV. Reinforcement Learning A. Basics of Reinforcement Learning

1. Learning process (agent, environment, reward signal)

2. Markov Decision Processes

3. Exploration vs Exploitation B. Algorithms

4. Q-Learning

5. Deep Q-Networks

6. Policy Gradient Methods

V. Advanced Topics A. Hyperparameter Tuning

1. Grid Search

2. Random Search

3. Bayesian Optimization B. Regularization

4. L1 Regularization

5. L2 Regularization C. Gradient Descent

6. Batch Gradient Descent

7. Stochastic Gradient Descent

8. Mini-Batch Gradient Descent D. Ensemble Methods

9. Bagging

10. Boosting

11. Stacking E. Transfer Learning F. Explainable AI

12. LIME

13. SHAP