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Other Applied Science Fields
Transportation
Autonomous Vehicles (Self-Driving Cars)
1. Introduction to Autonomous Vehicles
2. Sensor Technologies and Hardware Components
3. Environmental Perception and Scene Understanding
4. Localization and Mapping
5. Path Planning and Decision Making
6. Vehicle Control Systems
7. Artificial Intelligence and Machine Learning
8. System Integration and Communication
9. Testing, Validation, and Verification
10. Safety, Security, and Reliability
11. Regulatory, Legal, and Societal Aspects
Artificial Intelligence and Machine Learning
Machine Learning Fundamentals
Supervised Learning Applications
Classification Problems
Regression Problems
Training Data Requirements
Unsupervised Learning Applications
Clustering for Scene Understanding
Dimensionality Reduction
Anomaly Detection
Reinforcement Learning Applications
Policy Learning for Driving
Reward Function Design
Multi-Agent Reinforcement Learning
Deep Learning Architectures
Convolutional Neural Networks (CNNs)
Image Classification Networks
Object Detection Networks
Semantic Segmentation Networks
Real-Time CNN Optimization
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM)
Gated Recurrent Units (GRUs)
Sequence-to-Sequence Models
Transformer Architectures
Attention Mechanisms
Vision Transformers
Multi-Modal Transformers
Generative Models
Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Diffusion Models
Specific AI Applications
Computer Vision Tasks
Object Detection and Tracking
Depth Estimation
Optical Flow Estimation
Scene Flow Estimation
Natural Language Processing
Voice Command Processing
Traffic Sign Text Recognition
Map Annotation Processing
Time Series Analysis
Sensor Data Processing
Predictive Maintenance
Driving Pattern Analysis
Training Data and Datasets
Data Collection Strategies
Real-World Data Logging
Simulation-Based Data Generation
Synthetic Data Creation
Data Annotation and Labeling
Manual Annotation Processes
Semi-Automated Labeling
Active Learning Approaches
Dataset Challenges
Long-Tail Problem
Edge Case Coverage
Data Privacy and Security
Data Augmentation Techniques
Image Augmentation Methods
Synthetic Scenario Generation
Domain Adaptation Techniques
Model Training and Optimization
Training Methodologies
Transfer Learning
Fine-Tuning Strategies
Multi-Task Learning
Model Compression and Optimization
Quantization Techniques
Pruning Methods
Knowledge Distillation
Hardware Acceleration
GPU Optimization
TPU Deployment
Edge Computing Optimization
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8. System Integration and Communication