Machine Learning & AI training for R&D Teams

Hands-on, taught by a practising scientist.

For engineering and R&D teams in ML, AI, algorithm, vision, metrology, semiconductor, and hardware companies.

Plan a workshop for your team Browse workshops

Portrait of Yoav Ram

About the instructor

  • Assistant Professor, Faculty of Life Sciences, Tel Aviv University
  • Leads a lab that uses AI, statistics and mathematical models to study evolution, ecology and animal communication
  • Postdoc at Stanford University; PhD in Mathematical & Computational Biology, Tel Aviv University
  • Teaching Python and Data Science since 2011, in academia and industry; Excellence in Teaching awards from Tel Aviv University and Reichman University

Email Yoav

Teams I have trained

  • KLA
  • Applied Materials
  • Lam Research
  • Nova
  • Mobileye
  • Intuit
  • Houzz
  • trellis.ag
  • BlueVine
  • SAIPS

How I teach

  • Hands-on and interactive. Workshops are taught with Jupyter notebooks, in VS Code.
  • Short and long exercises. Short exercises to practise each idea, and longer ones to put several together.
  • No slides. All the code is shown and discussed.
  • Theory and application. Each topic is taught together with how to apply it.
  • You keep the materials. All course material, and access to the lecture recordings for a few months.
  • Groups of 5–50. In English or Hebrew.

Workshops

Eight workshops, each of which can be taught on its own or combined with others.

Workshops can be held on-site, in person, or online (Zoom or Teams, in 3-hour meetings).

Custom topics on request.

Introduction to Deep Learning

For developers and engineers who want to really understand neural networks.

  • Derive linear and logistic regression, and generalized linear models, from maximum likelihood.
  • Build and train feed-forward networks and CNNs for images and time series.
  • Implement backpropagation by hand, and diagnose why a network trains badly or overfits.

Duration: 3 days

Advanced Deep Learning

The next step after the introduction: transfer learning, generative models, imbalanced data and calibration.

  • Train classifiers when one class is rare, and calibrate their predicted probabilities.
  • Adapt pretrained models with transfer learning and fine-tuning.
  • Learn from unlabelled data with generative models: autoencoders, GANs and normalizing flows.

Duration: 2 days

Open-Set Learning: Metric Learning, Zero-Shot and Re-Identification

For teams whose models must handle classes, individuals or items they have not seen.

  • Explain closed-set versus open-set classification, and evaluate on classes the model never saw.
  • Classify zero-shot with CLIP, and learn your own embedding with metric learning.
  • Build and evaluate a retrieval pipeline, such as re-identifying individuals across photos.

Duration: 2 days

Scientific Python and Data Science

For engineers and developers new to Python, including engineers moving from MATLAB®.

  • Write idiomatic Python for numerical work.
  • Analyse data, signals, images and time series with NumPy, Pandas and SciPy.
  • Test your code and set up reproducible environments.

Duration: 2–4 days

Modelling and Simulation

For engineers and scientists who model dynamic systems.

  • Build and solve dynamical models, and analyse their stability.
  • Simulate stochastic processes in discrete and continuous time, and make the simulations fast.
  • Fit models to data with maximum likelihood.

Duration: 1–2 days

Bayesian Inference

For engineers and scientists who fit models to data.

  • Explain Bayesian inference and Markov Chain Monte Carlo (MCMC).
  • Fit dynamic models with Bayesian inference.
  • Infer parameters from simulations, with ABC and neural density estimation.

Duration: 1–2 days

ML with Coding Agents

For R&D teams adopting AI-assisted coding.

  • Specify tasks so an agent's output can be checked.
  • Review and verify agent-written code.
  • Deliver an ML analysis with an agent, checking it for errors that look plausible but are wrong.

Duration: 1 day

Custom Workshops and Consulting

For topics and problems that the workshops above do not cover.

  • A workshop built around your team's topics and, optionally, your own data under NDA.
  • Consulting in data science and scientific computing.

Testimonials

Contact

Tell me about your team and what you would like them to be able to do. A few lines are enough to start; I will reply to set up a scoping call.

Email Yoav yoav@yoavram.com