Workshop

Minds, Machines
and Matrices

A five-day, hands-on programme from DSAIL at Dedan Kimathi University of Technology, taking participants from the mathematical foundations of machine learning through to deployable, real-world systems.

Each day pairs two theory sessions with one practical lab — moving from matrix foundations and regression, through classification, unsupervised learning and embedded IoT systems, to neural networks and deployment.

The arc of the week
  1. Mathematical foundations
  2. Predictive models
  3. Systems & sensing
  4. Neural networks
  5. Deployment
See the full programme ↓
Venue Nelion Dome, DeKUT
Dates July 6 - 10, 2026
Daily hours 9:00 AM – 4:30 PM
Breaks 10:30 AM · 12:30 PM · 4:00 PM
Programme

The programme runs across five days. Days 1–4 each open with two theory sessions before moving into a practical lab; Day 5 closes the workshop with a deployment session and open Q&A. Click any topic below to open its content page. Session times are provisional and will be confirmed closer to the event.

Σ Theory conceptual sessions ▶ Practical hands-on lab work
Day 1

ML Basics & Regression

Day 2

Classification Algorithms

  • Session 1 · Theory 9:00 – 10:30 AM Logistic Regression

    Probability foundations, cross-entropy loss, and multiclass classification.

  • Session 2 · Theory 11:00 AM – 12:30 PM Decision Trees & Ensembles

    Decision trees, entropy, and ensemble methods (bagging & boosting).

  • Session 3 · Practical 2:00 – 4:00 PM Day 2 hands-on lab

    Building and evaluating classification models in a guided notebook.

Day 3

Unsupervised Learning & IoT Systems

  • Session 1 · Theory 9:00 – 10:30 AM Unsupervised Learning

    Clustering, dimensionality reduction, and anomaly detection.

  • Session 2 · Theory 11:00 AM – 12:30 PM IoT & Data Acquisition

    MCU communication protocols, wireless sensor networks (LoRaWAN), and hardware design.

  • Session 3 · Practical 2:00 – 4:00 PM Day 3 hands-on lab

    Clustering exercises and a guided IoT/hardware lab session.

Day 4

Neural Networks

  • Session 1 · Theory 9:00 – 10:30 AM Neural Network Foundations

    Perceptrons, activation functions, backpropagation, and automatic differentiation.

  • Session 2 · Theory 11:00 AM – 12:30 PM Advanced Neural Networks

    Convolutional neural networks (CNNs) and transformers.

  • Session 3 · Practical 2:00 – 4:00 PM Day 4 hands-on lab

    Building and training a neural network in a guided notebook.

Day 5

Deployment & Wrap-up

  • Session 1 · Theory 9:00 – 10:30 AM Deployment

    Taking trained models from notebook to production-ready systems.

  • Session 2 · Theory 11:00 AM – 12:30 PM Q&A

    Open floor to review concepts and discuss participant projects.