DAML Training Programs

Two pathways, one integrated build team

Members can specialize or rotate across the AI and infrastructure tracks. Shared milestones ensure that experimentation, deployment, and documentation stay in sync from the first workshop through the final showcase.

Machine Learning

AI Training Program (AITP)

Semester-long immersion that walks members from statistical learning foundations through deep learning, culminating in a certified client-ready portfolio.

Eight core workshops covering regression, clustering, ensembles, NLP, vision, RNNs, and transformers.

Final project built with a DAML pod, spanning scoping, experimentation, and executive-ready storytelling.

AITP certification unlocks placement on advanced research and client engineering pods.

Deployment

DevOps & Data Engineering

Learn deployment, data pipelines, loading into data warehouses, and more.

Cloud platforms like AWS, CI/CD, containerization

AITP Fall 2025 syllabus at a glance

Foundational weeks build statistical intuition, the mid-semester project track keeps momentum, and the final weeks dive deep into modern deep learning architectures.

Week 1How machines learn

Gradient descent, loss functions, bias-variance tradeoff, and structural risk minimization.

Hyperparameter tuning, cross-validation, train-test splits, and model evaluation.

Hands-on with linear and logistic regression, k-nearest neighbors, and SVMs.

Week 2Data science pipeline

Cleaning with emphasis on missing data and encodings that fight the curse of dimensionality.

Text and image preprocessing plus exploratory data analysis methods like ROC analysis and Pearson correlations.

After Week 2Choose your final project track

Members select a real partner-aligned brief and form project teams with DAML mentors.

Milestones, tech stack guardrails, and accountability cadences are set before Week 3.

Week 3Dimension reduction and clustering

Principal component analysis and manifold learning techniques like MDS, Isomap, spectral clustering, and t-SNE.

Clustering with k-means and expectation-maximization.

Week 4Ensemble methods and boosting

Decision trees, information theory, random forests, AdaBoost, XGBoost, and generalized additive models.

Week 5Neural network fundamentals

Architecture design, activation functions, universal approximation, and backpropagation.

Week 6Convolutional neural networks

Convolutional layers, kernels, pooling strategies, and computer vision applications.

Week 7Recurrent neural networks and LSTMs

Motivation, vanishing gradients, and LSTM gate mechanics for NLP workflows.

Week 8Transformers and LLMs

Transformer architecture, semi-supervised fine-tuning, and practical BERT + LLM deployments.

Prerequisites

Comfort with Python from CS 101/201 or equivalent experience.

Prior exposure to high school or college-level CS courses is helpful but not required.

Growth mindset and a willingness to collaborate in fast-paced project teams.

Weekly cadence

Workshops: Saturdays 2:00-3:00pm in SocSci 139 (hybrid option available).

Project work sessions: Saturdays 3:00-4:00pm in SocSci 139 with mentor check-ins.

Time cards document hours spent, progress, and GitHub commits for accountability.

Accountability

More than one unexcused workshop absence removes eligibility for certification.

Notify division leads early if a permanent scheduling conflict exists; recordings provided when approved.

Peer evaluations and final deliverables determine advancement to Senior Engineer roles.

Final deliverables

Project showcase with faculty and partner judges at semester end.

Short final exam covering AITP content to validate mastery.

Weighted score across presentation and exam informs placement on advanced engagements.

Deliver, present, certify

Completing workshops, milestones, showcase presentations, and the final exam earns the AITP certification. Certified members flow directly into DAML senior engineering roles and partner-facing pods.

See how training fuels R&D