machine learning courses

[martinjrobins]

Suggested list of courses would be:

• An introduction to deep learning **
  • How to train a neural network
  • Regularisation in neural networks
  • Deep Bayesian neural networks
  • Convolutional neural networks
  • Graph neural networks
  • Recurrent neural networks
  • Transformer-based architectures and Large Language Models**
  • Convolutional neural networks
  • An introduction to PyTorch
  • An introduction to Tensorflow
  • How to use pretrained neural networks
  • An introduction to generative deep learning:**
    • Reinforcement Learning
    • GANNs
    • Variational autoencoders
    • Diffusion models
• Other supervised learning methods
  • Linear Models
  • Random Forests and other Tree-based Methods
  • Support Vector Machines
• Clustering:
  • K-means
  • Gaussian mixture modelling and Dirichlet process models
• Dimensionality Reduction:
  • Projection methods: PCA and sparse-PCA-type methods
  • Manifold-type methods: t-SNE and UMAP
• Semi-supervised and Active Learning
• Model Selection and Model Optimization (e.g. optuna)
• Feature Extraction and Feature Selection
• Model Evaluation
  • Metrics
    • Classification
    • Regression
    • Clustering
  • Model Uncertainty (see: Gaussian Processes)
  • Generalizability & Domain of Applicability
  • Cross-fold Validation
  • Common Pitfalls and Reproducibility
• Automated Machine Learning
  • Classical Automated ML
    • AutoGluon
    • FLAML
  • LLM-based Multi-Agent Frameworks
    • autogen
    • CrewAI
    • TaskWeaver
  • LLM Orchestration
    • LangChain
    • LangGraph

[martinjrobins]

Ben has some intro to unsupervised and supervised ml here: intro to unsupervised and supervised ml