Welcome to Caltech's AI/ML Lab for Engineering and Science, where we propel your machine learning skills to new heights for industry-grade applications in both research and real-world product environments. Our intensive, hands-on five-day certificate program is designed to hone your expertise based on the Institute's proven methodology for nurturing researchers' capabilities.
Welcome to Caltech's AI/ML Lab for Engineering and Science, where we propel your machine learning skills to new heights for industry-grade applications in both research and real-world product environments. Our intensive, hands-on five-day certificate program is designed to hone your expertise based on the Institute's proven methodology for nurturing researchers' capabilities.
Program Experience
In this program, you will delve into the foundations of machine learning algorithms, gaining a deep understanding of data preprocessing, model selection, and evaluation criteria. Through diverse use cases, we will guide you along the entire ML lifecycle, arming you with a potent toolkit to conquer industry challenges with confidence.
Our aim is to provide you with practical, applied machine learning techniques that you can implement immediately. While we will touch upon the statistical foundations of modern ML, our primary focus will be on equipping you as a real-life industry practitioner in engineering and research domains.
This course is tailored for technically adept learners who wish to gain a comprehensive understanding of the entire AI/ML lifecycle, exploring the intricate trade-offs between commercial demands and academic pursuit of algorithm development. Embark on this transformative journey with us and unlock your full potential in the realm of AI and machine learning.
Benefits
Participants will advance their existing machine learning skills and be able to:
Understand industry applications of the end-to-end ML lifecycle
Complete the foundations of ML with analytical methods and statistical deep dives
Preprocess data to fit the needs of modern ML algorithms
Understand the entire ML lifecycle and its applications
Create robust modelling of supervised and unsupervised algorithms
Know which algorithm to select for various real-world scenarios
Approach deep learning with applied knowledge of neural networks
Leverage deep learning methods using modern tools like PyTorch
Know the differences in compute when using deep learning
Apply foundational large language models (LLMs) to current use cases
Topics
The course covers the entire machine learning lifecycle and the toolkit needed to create robust machine learning pipelines:
Data preprocessing techniques
Statistical foundations of ML
Exploratory Data Analysis
Python best-practices
Modern ML libraries
Model selection and hyperparameter tuning
Supervised ML algorithms
Unsupervised techniques
ML pipelines
Data bias and how to avoid it in modelling
Dimensionality reduction toolkit
Deep Learning with neural networks
PyTorch application of deep learning
Bias-Variance Trade-off
Natural Language Processing (NLP)
NLP and deep learning applied to generative AI (LLMs)
Leverage deep learning techniques such as FFNNs, RNNs, LSTMs
The modern ML lifecycle
MLOps and ML deployment to production
Who Should Attend
This program is designed for experienced professionals with a background in engineering, science, or related fields such as aerospace, chemistry, biology, electronics, finance, communications, or technology. It is ideal for those who want to integrate data science and machine learning into their work. Learners are expected to have a solid understanding of calculus, linear algebra, probability, statistics, and basic programming skills, including Python/R. The course provides a balanced combination of theoretical concepts and practical applications.
