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Data Science
Tabular Foundation Models – TabPFN, In-Context Learning and Bayesian Inference for Structured Data
📋 KEY INSIGHTSTabular foundation models (TFMs) pre-train on large collections of tabular datasets and perform in-context learning at inference time — classifying or regressing on a new dataset by...
Data Science
Reward Modelling and RLHF Deep Dive – DPO, Constitutional AI and Preference Learning
📋 KEY INSIGHTSReward modelling is the process of learning a scalar reward function from human preference data, used in Reinforcement Learning from Human Feedback (RLHF) to align language models...
Data Science
Data Science for Manufacturing – Predictive Maintenance, Quality Control and Digital Twins
📋 KEY INSIGHTSData science for manufacturing focuses on four high-value problem areas: predictive maintenance (predicting equipment failures before they cause unplanned downtime), quality control (detecting defective products and identifying...
Data Science
Geometric Deep Learning – E(3)-Equivariant GNNs, PointNet and SE(3)-Transformers
📋 KEY INSIGHTSGeometric deep learning (GDL) is the unifying framework that extends deep learning to data with non-Euclidean structure — graphs, point clouds, meshes, manifolds, and 3D geometric objects...
Data Science
Machine Learning for Drug Discovery – Molecular GNNs, ADMET Prediction and Generative Chemistry
📋 KEY INSIGHTSMachine learning for drug discovery applies predictive modelling to accelerate the identification, optimisation, and development of drug candidates. The drug discovery pipeline has four stages where ML...
Data Science
Structured State Space Models – S4, Mamba, Selective SSMs and Long-Range Sequence Modelling
📋 KEY INSIGHTSStructured State Space Models (SSMs) are a family of sequence models that map input sequences to output sequences via a latent state updated by a linear ODE:...
Data Science
Data Science for Retail – Price Elasticity, Demand Forecasting, Markdown and Assortment Optimisation
📋 KEY INSIGHTSData science for retail addresses decisions across the retail value chain: pricing (setting and updating prices to maximise revenue or profit), assortment (deciding which products to carry...
Data Science
Optimal Transport in Machine Learning – Wasserstein Distance, Sinkhorn and Domain Adaptation
📋 KEY INSIGHTSOptimal transport (OT) is a mathematical framework for comparing probability distributions by computing the minimum cost required to transport mass from one distribution to another. The Wasserstein...
Data Science
Foundation Models Beyond LLMs – Time Series, Vision, Protein and Multimodal Foundation Models
📋 KEY INSIGHTSFoundation models are large-scale models pre-trained on broad, diverse datasets using self-supervised learning, then fine-tuned or zero-shot adapted for downstream tasks. While the term is most associated...
Data Science
Neural ODEs and Physics-Informed Neural Networks – PINNs, Neural Operators and Scientific ML
📋 KEY INSIGHTSNeural Ordinary Differential Equations (Neural ODEs, Chen et al., NeurIPS 2018) define a continuous-depth neural network by replacing the discrete layer-to-layer transformation of a residual network with...
Data Science
Data Science for HR – People Analytics, Attrition Prediction, Pay Equity and Workforce Planning
📋 KEY INSIGHTSPeople analytics (HR analytics, workforce analytics) applies data science to human resources decisions: predicting which employees are at risk of leaving (attrition prediction), optimising compensation structures (pay...
Data Science
Probabilistic Forecasting – Quantile Regression, Conformal Prediction, NGBoost and CRPS
📋 KEY INSIGHTSProbabilistic forecasting produces a full predictive distribution over future values — not a single point estimate — enabling decision-makers to reason about forecast uncertainty, compute confidence intervals,...
Data Science
LLM Evaluation – MMLU, HumanEval, MT-Bench, LLM-as-Judge and Red-Teaming
📋 KEY INSIGHTSLLM evaluation is the systematic process of measuring a language model's capabilities, limitations, and alignment — covering knowledge and reasoning benchmarks (MMLU, HellaSwag, ARC), code generation (HumanEval,...
Data Science
Reinforcement Learning for Real-World Applications – Offline RL, Reward Shaping and Safe RL
📋 KEY INSIGHTSReal-world reinforcement learning differs fundamentally from the game-playing and simulation settings where RL research has progressed most rapidly. Production RL must deal with: partial observability (the agent...
Data Science
Data Science for Cybersecurity – Intrusion Detection, Malware Classification and Log Anomaly
📋 KEY INSIGHTSData science for cybersecurity applies ML to three core problem types: intrusion detection (identifying malicious network traffic or host behaviour from logs and packet captures), malware analysis...
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