Ken Kennedy Institute AI and Machine Learning Boot Camp
May 7–9, 2025 | BioScience Research Collaborative at Rice University
Registration: Details coming soon
| Day 1 | May 7 | Sessions |
|---|---|
| 8:30–9:00 AM | Check-in + Breakfast |
| 9:00–10:30 AM | Topic: Intro to Machine Learning (ML) Instructor: Chris Jermaine |
| 10:30–11:00 AM | Break |
| 11:00 AM–12:30 PM | Topic: Basic ML Instructor: Santiago Segarra |
| 12:30–1:30 PM | Break |
| 1:30–3:00 PM | Topic: Deep Learning Instructor: Anastasios (Tasos) Kyrillidis |
| 3:00–3:30 PM | Break |
| 3:30–5:00 PM | Topic: Advanced Deep Learning Instructor: Anastasios (Tasos) Kyrillidis |
| Day 2 | May 8 | Sessions |
| 8:30–9:00 AM | Check-in + Breakfast |
| 9:00–10:30 AM | Topic: Decentralized ML Instructor: Cesar Uribe |
| 10:30–11:00 AM | Break |
| 11:00 AM–12:30 PM | Topic: Natural Language Processing (NLP) Instructor: Hanjie Chen |
| 12:30–1:30 PM | Break |
| 1:30–3:00 PM | Topic: Interpretable ML – ML Systems Instructor: Xia (Ben) Hu |
| 3:00–3:30 PM | Break |
| 3:30–5:00 PM | Topic: TBA Instructor: TBA |
| Day 3 | May 9 | Sessions |
| 8:30–9:00 AM | Check-in + Breakfast |
| 9:00–10:30 AM | Topic: Reinforcement Learning (RL) Instructor: Vaibhav Unhelkar |
| 10:30–11:00 AM | Break |
| 11:00 AM–12:30 PM | Topic: Hands-on RL Instructor: Vaibhav Unhelkar |
| 12:30–1:30 PM | Break |
| 1:30–3:00 PM | Topic: Large Language Models (LLMs) Instructor: Anshu Shrivastava |
| 3:00–3:30 PM | Break |
| 3:30–5:00 PM | Topic: Advanced Deep Learning Instructor: Anshu Shrivastava |
View information about previous versions of the boot camp below.
2024 Focus: Machine Learning for Executives and Beginners
- Location: BioScience Research Collaborative at Rice University | Houston, TX
- Topics: Fundamentals of ML, basic methods, deep learning and advanced deep learning
- 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.
The program highlights 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 provides practical suggestions to executives with dos and don'ts when using ML in practice.
More information about previous boot camp versions available at: http://bootcamp.rice.edu/
