Introduction to Computer Science

  1. Artificial Intelligence and Machine Learning
    1. Artificial Intelligence Overview
      1. Defining Artificial Intelligence
        1. Intelligence in Machines
          1. Cognitive Capabilities
          2. AI Categories
            1. Narrow AI (Weak AI)
              1. General AI (Strong AI)
                1. Artificial Superintelligence
                2. AI Applications
                  1. Expert Systems
                    1. Natural Language Processing
                      1. Computer Vision
                        1. Robotics
                          1. Game Playing
                        2. Machine Learning Fundamentals
                          1. Machine Learning Definition
                            1. Learning from Data
                              1. Pattern Recognition
                                1. Prediction and Decision Making
                                2. Types of Machine Learning
                                  1. Supervised Learning
                                    1. Classification Problems
                                      1. Regression Problems
                                        1. Training with Labeled Data
                                          1. Common Algorithms
                                            1. Linear Regression
                                              1. Decision Trees
                                                1. Support Vector Machines
                                                  1. Neural Networks
                                                2. Unsupervised Learning
                                                  1. Clustering
                                                    1. Association Rules
                                                      1. Dimensionality Reduction
                                                        1. Pattern Discovery
                                                        2. Reinforcement Learning
                                                          1. Agent-Environment Interaction
                                                            1. Reward-Based Learning
                                                              1. Policy Optimization
                                                            2. Machine Learning Process
                                                              1. Data Collection
                                                                1. Data Preprocessing
                                                                  1. Feature Selection
                                                                    1. Model Training
                                                                      1. Model Evaluation
                                                                        1. Model Deployment
                                                                        2. AI Ethics and Considerations
                                                                          1. Algorithmic Bias
                                                                            1. Fairness in AI
                                                                              1. Transparency and Explainability
                                                                                1. Privacy Concerns
                                                                                  1. Job Displacement
                                                                                    1. AI Safety

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                                                                                  11. Human-Computer Interaction