machine-learning-research/learning-path.md at master · imteekay/machine-learning-research

ML/AI Learning Path Structured Learning Path: ML/DL in Biomedicine Core training Papers Top-5 papers in each subfield of interest Implementation Implement paper and architectures of ML models Foundational Mathematics Build the mathematical foundation for ML Fill the knowledge gaps from reading papers and technical articles Foundational Biology Molecular Biology Genetics Proteins Advanced ML of field of interest Multimodal ML Multimodal Deep Learning Transformer Architecture Deep Reinforcement Learning Diffusion Models ML Systems Code Read, unpack & review source code from papers Read, unpack & review source code from ML models List RoseTTaFold AlphaFold Sybil ESM-2 TxGemma (ref1, ref2) C2S (ref1) Med-PaLM Large Language Models Encode Clinical Knowledge Towards Expert-Level Medical Question Answering with Large Language Models Towards Generalist Biomedical AI I really need to Read papers (to build the foundation, produce the knowledge gaps) Read technical articles (to build the foundation, produce the knowledge gaps) Table of Contents ML/AI Learning Path Table of Contents Programming & Engineering Math & Statistics Foundations Foundation in Machine Learning Foundation in Deep Learning RL Biomedicine and Healthcare Machine Learning in Biomedicine Programming & Engineering Python Python Basics Fluent Python Numpy Pandas Scikit-learn Data Engineering Feature Engineering Data Cleaning PySpark ML Systems [Book] Introduction to Machine Learning Systems Deep Learning Frameworks Introduction to Neural Networks and PyTorch Learn PyTorch for Deep Learning PyTorch for Deep Learning PyTorch: Fundamentals PyTorch: Techniques and Ecosystem Tools PyTorch: Advanced Architectures and Deployment Math & Statistics Foundations [Book] Mathematics of Machine Learning Math Academy Linear Algebra Calculus Probability and Statistics Introduction to Probability Practical Statistics for Data Scientists Foundation in Machine Learning Machine Learning Specialization [Course] Supervised Machine Learning: Regression and Classification [Course] Advanced Learning Algorithms [Course] Unsupervised Learning, Recommenders, Reinforcement Learning Machine Learning Models Linear Regression Logistic Regression Decision Trees Decision Tree from Scratch Model Evaluation Accuracy, Loss, ROC AUC, Precision, Recall Causal Inference [Book] Causal Inference: What If Machine Learning Engineering Machine Learning in Production Multimodal Machine Learning [Course] CMU Multimodal Machine Learning [Paper] Multimodal Readings [Paper] Scaling Laws for Native Multimodal Models Geometric Machine Learninig [Book] Graph Neural Networks Foundation in Deep Learning Deep Learning Specialization [Course] Neural Networks and Deep Learning [Course] Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization [Course] Structuring Machine Learning Projects [Course] Convolutional Neural Networks [Course] Sequence Models Deep Learning Neural Networks Deep Neural Networks Convolutional Neural Networks (CNNs) VGG ResNet U-Net Recurrent Neural Networks (RNNs) Recurrent Neural Network (RNN Lab) LSTMs GRUs Transformers [Course] Attention in Transformers [Course] How Transformer LLMs Work [Notebook] Transformers Encoder from scratch with PyTorch [Book] Understanding Deep Learning: Transformers Chapter [Book] Transformers and Large Language Models [Course] Stanford Language Modeling From Scratch Assinment 1: Building a Transformer LM: PDF Assinment 2: Systems and Parallelism: PDF Assinment 3: Scaling Laws: PDF Assinment 4: Filtering Language Modeling Data: PDF Assinment 5: Alignment and Reasoning RL: PDF [Course] LLM Course by Hugging Face Mixture of Experts (MoE) [Paper] A Survey on Mixture of Experts in Large Language Models Generative AI Autoencoders/Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) Diffusion Models Graph Neural Networks (GNNs) Papers Mixtral of Experts Vision Transformer PPO Model Distillation Graph Attention Networks RL Deep Reinforcement Learning [Course] Deep Reinforcement Learning - Lecture Foundations of Deep Reinforcement Learning book Code Biomedicine and Healthcare Genetics Fundamentals [Course] Genetics: The Fundamentals [Course] Genetics: Analysis and Applications [Course] DNA Decoded Genomics Papers Molecular Biology [Book] Molecular Biology of the Cell [Book] Molecular Biology for Computer Scientists [Book] Molecular Biology: A Very Short Introduction [Book] A Computer Scientist’s Guide to Cell Biology [Primer] A Biology Primer for Computer Scientists [Graduation] Bioinformática Aplicada a Genômica Médica - Expressão Gênica, Metagenômica e Machine Learning [Graduation] Bioinformática Aplicada a Genômica Médica - Análise de Variantes Germinativas e Somáticas [Graduation] Medicina de Precisão e Análise de Dados na Saúde Cancer Fundamentals Cancer Biology Specialization Machine Learning in Biomedicine AI/ML in Healthcare [Course] MIT Machine Learning in Computational Biology Lectures Document Course NotebookLM Machine Learning for Healthcare AI for Medicine Specialization AI for Medical Diagnosis AI for Medical Prognosis AI For Medical Treatment AI in Healthcare Specialization Collaborative Data Science for Healthcare Data Analytics and Visualization in Health Care [Course] Deep Learning for Healthcare Specialization: labs Health Data Science Foundation Deep Learning Methods for Healthcare Advanced Deep Learning Methods for Healthcare [Graduation] Medicina de Precisão e Análise de Dados na Saúde Introduction to Applied Biostatistics: Statistics for Medical Research Genomic Data Science Bioinformatics Data Preprocessing NGS data formats (FASTA/FASTQ/VCF) Quality control (FastQC) Pipelines (alignment, normalization) Biopython Genomic Data Science Specialization [Course] Introduction to Genomic Technologies Introduction to Genomic Technologies Notes [Course] Python for Genomic Data Science [Course] Algorithms for DNA Sequencing [Course] Command Line Tools for Genomic Data Science [Course] Bioconductor for Genomic Data Science [Course] Statistics for Genomic Data Science [Book] Bioinformatics Algorithms: An Active Learning Approach Introduction to Genomic Data Science [Course] HarvardX Biomedical Data Science Data Analysis for Life Sciences Data Analysis for Genomics Using Python for Research AI/ML in Biology for Medicine Protein Structure Highly accurate protein structure prediction with AlphaFold Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences ProtST: Multi-modal Foundation Model for Protein Representation Learning Protein Dynamics Simulation Scaling deep learning for materials discovery DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning Accurate structure prediction of biomolecular interactions with AlphaFold 3 BioEmu: AI‐Powered Revolution in Scalable Protein Dynamics Simulation Experimental Data Driven AI Framework for Flexible Protein Conformational Reconstruction Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence MELD in Action: Harnessing Data to accelerate Molecular Dynamics

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ML/AI Learning Path

Table of Contents

Programming & Engineering

Math & Statistics Foundations

Foundation in Machine Learning

Foundation in Deep Learning

RL

Biomedicine and Healthcare

Machine Learning in Biomedicine

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