Curriculum

2023 Focus: Machine Learning for Executives and Beginners

Machine Learning Boot Camp – May 11-12, 2023
BioScience Research Collaborative at Rice University | Houston, TX
Thursday – Friday |  8:00 a.m. – 5:00 p.m. CST

Thursday, May 11 Schedule (Day One):
8:00-8:30 am – Check-In with Breakfast
8:30-10:00 am – Fundamentals of Machine Learning with Chris Jermaine
10:00-10:20 am – Break
10:20-12:05 pm – Fundamentals of Machine Learning with Chris Jermaine
12:05-12:45 pm – Lunch
12:45-2:30 pm – Basic Methods with Santiago Segarra
2:30-3:00 pm – Break
3:00-5:00 pm – Basic Methods with Santiago Segarra
5:00 pm – End of Instruction

Friday, May 12 Schedule (Day Two):
8:00-8:30 am – Check-In with Breakfast
8:30-10:00 am – Deep Learning with Tasos Kyrillidis
10:00-10:20 am – Break
10:20-12:20 pm – Deep Learning with Tasos Kyrillidis
12:20-1:00 pm – Lunch
1:00-2:30 pm – Advanced Deep Learning with Tasos Kyrillidis
2:30-3:00 pm – Break
3:00-5:00 pm – Advanced Deep Learning with Tasos Kyrillidis
5:00 pm – End of Instruction

This two-day boot camp will provide a broad introduction to Machine Learning (ML) for executives and beginners. Our goal is to give a manager or CEO in various industries an introduction to the essential concepts behind Machine Learning. Additionally, this boot camp is great for non-practicing beginners looking to learn. Taught by a team of Rice professors in the computational sciences, the Machine Learning Boot Camp will integrate lectures describing the basic concepts to state-of-the-art Machine Learning.

The program will highlight a broad set of concepts, ranging from classical supervised (e.g., linear regression, logistic regression and decision trees) and unsupervised methods (e.g., clustering algorithms, principal components analysis) to modern machine learning approaches (e.g., different neural network architectures, min-max games and generative models, deep reinforcement learning), with numerous case studies and applications. Throughout its duration, the boot camp will provide practical suggestions to executives with DOs and DON’Ts when using Machine Learning in practice.

General prerequisites: A general level of familiarity with mathematics is assumed, including college-level calculus, though those without this background will still find the boot camp a useful introduction to modern ML.

This is an in person event only. The boot camp will not be live streamed or recorded.

By the end of the boot camp, participants will be familiar with the following topics:

  • What is ML? What is it good for?
  • Fundamentals of ML (training/testing/validation, loss functions, featurization, regularization), along with the basics of optimization.
  • ML models: linear regression, logistic regression, kernel methods, support vector machines, clustering methods, dimensionality reduction techniques.
  • Decision trees, random forests, ensemble methods, boosting and bagging.
  • Fundamentals of Deep Learning: basic feedforward neural networks and the perceptron, optimization methods in neural networks and backpropagation, convolutional neural networks, autoencoders and variational autoencoders, recurrent neural networks and LSTMs, generative models and GANS.
  • Reinforcement learning and its applications in deep learning.

Participants at all career stages are welcome to attend. Note that the boot camp is meant as an executive-level summary of ML; hands-on ML programming is not part of the curriculum.

Curriculum

Thursday, May 11, 2023

AM Session: Fundamentals of Machine Learning (Chris Jermaine)

  • What is ML?
  • History of ML –1950’s through present day
  • ML success stories
  • ML failures
  • Types of learning
  • Learning loss
  • Features, parameters, and fitting
  • Training, testing and validation

PM Session: Basic Methods (Santiago Segarra)

  • Linear and logistic regression
  • SVMs and kernels
  • Decision trees, random forests, ensemble methods (boosting and bagging)
  • Regularization
  • Clustering and dimensionality reduction

Friday, May 12, 2023

AM Session: Deep Learning (Tasos Kyrillidis)

  • The basics of neural networks: Multi-layer perceptron
  • Backpropagation and stochastic gradient descent
  • Convolutional neural networks
  • Recurrent neural networks

PM Session: Advanced Deep Learning (Tasos Kyrillidis)

  • Self-attention and Transformer-based neural networks
  • Pipeline of large language models
  • Generative AI
  • Overview of modern ML tools: TensorFlow and PyTorch