Roadmap Artificial Intelligence (AI) - Rian010/Journal GitHub Wiki
Artificial Intelligence (AI) adalah pengembangan perangkat lunak yang mampu melakukan tugas yang seharusnya hanya dapat dilakukan oleh manusia. Berikut adalah roadmap AI:
Bab 1: Matematika dan Statistik
Bagian 1: Linear Algebra
- Vector
- Matrix
- Determinants
- Eigenvalues and eigenvectors
Bagian 2: Calculus
- Derivatives
- Integrals
- Limits
- Optimization
Bagian 3: Probability and Statistics
- Bayesian probability
- Maximum likelihood estimation
- Hypothesis testing
- Regression analysis
Bab 2: Machine Learning
Bagian 1: Supervised Learning
- Linear regression
- Logistic regression
- Support vector machines
- Random forests
Bagian 2: Unsupervised Learning
- Clustering algorithms
- Dimensionality reduction
- Anomaly detection
Bagian 3: Deep Learning
- Neural networks
- Convolutional neural networks
- Recurrent neural networks
- Reinforcement learning
Bab 3: Natural Language Processing (NLP)
Bagian 1: Text Preprocessing
- Tokenization
- Stop words removal
- Lemmatization
- Part-of-speech tagging
Bagian 2: Word Embeddings
- One-hot encoding
- Word2Vec
- GloVe
- FastText
Bagian 3: Sequence Models
- Long short-term memory
- Gated recurrent units
- Transformers
- Attention mechanisms
Bab 4: Computer Vision
Bagian 1: Image Preprocessing
- Filtering
- Edge detection
- Feature extraction
- Augmentation
Bagian 2: Object Detection
- Region proposal networks
- Classification and bounding box prediction
- Non-max suppression
- Tracking objects
Bagian 3: Scene Understanding
- Semantic segmentation
- Instance segmentation
- Panoptic segmentation
- 3D reconstruction
Bab 5: Robotics
Bagian 1: Kinematics and Dynamics
- Forward kinematics
- Inverse kinematics
- Jacobians
- Dynamics equations
Bagian 2: SLAM
- Visual simultaneous localization and mapping
- LiDAR-based SLAM
- RGB-D SLAM
- Graph-based SLAM
Bagian 3: Planning and Control
- Trajectory generation
- Model predictive control
- Feedback linearization
- Observer design
Referensi: The Ultimate AI and Machine Learning Resource Guide Deep Learning Specialization by Andrew Ng