A Gentle Introduction To Ai For Chemical Engineers

What is AI and ML and what are basic principles behind building AI and ML models?

What you'll learn

Understand the definition of AI, Machine Learning, and other modeling approaches using simple ChemEng examples

Understand the core ideas and principles behind AI/ML methods, including neural networks

Identify the right approach to a modeling problem

Get a high-level understanding of how Large Language and Computer Vision models work

Requirements

No coding skill is required. This course is focused on understanding the core ideas and principles without any math and programming.

The only requirement is the familiarity with the ideal gas law.

Description

An introductory course designed for helping engineering and chemistry STEM students and industry professionals entering the data science, AI, and machine learning areas. This course is appropriate for those with minimal prior exposure to the field of AI and interested to either enter or shift their career path to this field and related areas. We use the simplest concepts in chemical engineering and chemistry, mainly the famous ideal gas law! to go over and introduce various topics related to AI and ML. In each step, we use simple, relevant, and area-specific examples to show how these concepts relate to real-world applications and systems in chemical engineering and chemistry fields.Main topics covered in the course include:Exact definition of AI and ML and the important terminology of the fieldMain differences between different modeling approaches from purely data-driven models to mechanistic modelsDefinition of loss function and importance of selecting an appropriate one,An introduction to artificial neural networks and deep learningOverview of vision and language modelsAn introduction to cloud computing and its benefits.The course concludes by going over several recommendations for taking the next steps necessary to continue your journey towards this dynamic, fast-growing, and exciting field.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: AI and Definitions

Lecture 2 What is AI? What is Machine Learning?

Lecture 3 Predictor and Response Variables

Lecture 4 More discussion of features

Lecture 5 Data-driven, Mechanistic, and Hybrid Modeling

Lecture 6 Different data types

Section 3: Building a Model: From Regression to Importance of the Loss Function

Lecture 7 Linear regression

Lecture 8 How to train a model and what is loss function

Lecture 9 Data-driven Vs. Mechanistic models

Lecture 10 Classification Vs. Regression

Section 4: Introduction to Deep Learning Models

Lecture 11 Introduction to neural networks

Lecture 12 On training neural networks

Lecture 13 Gradience descent method

Section 5: On Language and Vision Models

Lecture 14 Boom of deep learning models

Lecture 15 Text and image processing: Computer vision models

Lecture 16 Text and image processing: Language models

Lecture 17 GPT and other decoder Large Language Models (LLM)

Lecture 18 Complexities of deep learning models

Section 6: Brief Introduction to Cloud Computing

Lecture 19 Cloud computing and its benefits

Section 7: Final Remarks and Recommendations

Lecture 20 Final remarks and recommendations for next

STEM students, chemical and mechanical engineering, and chemistry major students interested in getting into data science, AI and machine learning areas.,Early-career chemical and mechanical engineers interested in AI, machine learning, and data science areas.