AI, ML/ Computer visions topics I'd like to be included in the workshop classes.

summary.md

Beginner:

• Introduction to machine learning and its applications
• Basic statistical concepts for ML
• Data preprocessing and feature engineering
• Supervised learning and classification algorithms
• Model evaluation and selection

Intermediate:

• Unsupervised learning and clustering algorithms
• Neural networks and deep learning
• Convolutional neural networks for computer vision
• Natural Language Processing and text classification
• Time series forecasting with machine learning

Advanced:

• Reinforcement learning and its applications
• Generative Adversarial Networks (GANs) and their applications
• Transfer learning and fine-tuning pre-trained models
• Hyperparameter tuning and optimization techniques
• Explainable AI and interpretability techniques

workshop_topic_suggestion.md

Reinforcement learning

• Markov decision processes
• Q-learning
• Policy gradients
• Model-based vs. model-free RL

Unsupervised learning

• Clustering
• Dimensionality reduction
• Autoencoders

Computer vision

• Convolutional neural networks
• Object detection and localization
• Image segmentation
• Transfer learning for image classification

Natural Language Generation

• Text generation using GPT-3 or similar models
• Conditional text generation
• Controllable generation

Explainability and interpretability in ML

• Techniques for understanding model decisions
• Local and global explanations
• Model distillation

Data preprocessing and feature engineering

• Techniques for handling missing values
• Feature selection and extraction
• Data normalization and scaling

Hyperparameter tuning and model selection

• Grid search
• Random search
• Bayesian optimization
• Cross-validation and overfitting

Advanced topics in ML

• Adversarial attacks and defenses
• Federated learning
• Meta-learning
• Reinforcement learning in games or robotics.

workshop_topic_suggestions.md

1. Natural Language Processing deep dive

• Attention models
• Probabilistic models
• Sequence models
• Classification and vector spaces

2. Building and applying GANs

3. ML, Deep learning algorithms

4. ML frameworks

5. Deep learning for timeseries forecasting

6. Reading ML papers

7. Building strong ML foundation