Undergraduate/Graduate Teaching Appointments

University of Chicago Booth School of Business

• Chief AI Officer Program | Official Link

Pace University

Please use this site Course Schedule Explorer to search for classes. Choose desired term, graduate level, and computer science as subject to be able to search classes I teach.

• CS 676 Algorithms for Data Science | YouTube Playlist | GitHub
• CS 668 Analytics Capstone Project | YouTube Playlist | GitHub
• CS 667 Practical Data Science | YouTube Playlist | GitHub

Columbia University

• GU STAT 4203 Probability Theory (Fall 2021, Summer 2021, Fall 2020, Fall 2018) | Updated Notes.
• GU STAT 5204 Statistical Inference (Fall 2021) | Updated (handwritten) Notes, Recitation here.
• GU STAT 5241 Statistical Machine Learning (Spring 2019) | Repo here

Pre-college Teaching Appointments

• Pre-College Big Data, Machine Learning, and Real World Application, Course Github | Previous Lectures | Student Presentation | These are the main resources I use for high school and pre-college level AI education.
• Syllabus (in yearly order):
  • Summer 2022
  • Summer 2021
• Assignment Evaluations: Instead of homework or assignments, please use the following forms to communicate progress report.
  • Orientation: Please fill this form out by the first session of the program.
  • Session Survey: Please fill this form out at the end of each session.
• Machine Learning General | Please feel free to check out the ML General repo I have been using for undergraduate teaching.
• Deep Learning Notebooks | Please feel free to check out the Deep Learning notebooks I have been using for undergraduate and pre-college level students.
• Lead Curriculum Design at Veritas AI | Please feel free to check out the company's website. It's founded by Harvard PhD students.

Public Teaching Appointments

AI4ALL: General Machine Learning and Artificial Intelligence FREE Sources

• The Fundamentals in Machine Learning | Link | This is an introduction course of machine learning: The Fundamentals of Machine Learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.
• Random Graphs and Complex Networks | Link
• An introduction to Optimization on smooth manifolds | Link
• Computer Age Statistical Inference: Algorithms, Evidence and Data Science | Link
• Statistical Learning with Sparsity: The Lasso and Generalizations | Link
• The Shallow and the Deep: A biased introduction to neural networks and old school machine learning | Link
• User-friendly Introduction to PAC-Bayes Bounds | Link
• Geometric Mechanics Part I: Dynamics and Symmetry | Link
• Artificial Intelligence: Foundations of Computational Agents | Link
• List of curated books | Link
• AI4ALL | Github: link
• Fundamentals in Neural Networks | Link | Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks.
• Basics in Artificial Neural Networks | Link | The course introduces the fundamental building blocks of an Artificial Neural Network (ANN) model. With ANN being the leading milestone, the course lays the ground work for the audience into the field of Representation Learning.
• Basics in Convolutional Neural Networks | Link | The course expands from ANN and introduces the fundamental building blocks of a Convolutional Neural Network (CNN). Advanced CNN models are also introduced to lead audience to the field of Representation Learning.
• Image-to-Image Network Models | Link | The course investigates a higher level of network models that learn the intrinsic representation of image data. Such models learn to produce images rather than annotations or labels, which is different from previous courses. The materials lead the audience into the field of unsupervised learning.
• Natural Language Processing | Link | The course investigates machine intelligence on language interpretations. Moreover, we investivate deep recurrent network models to study and potentially make predictions using language as input.
• Online Extension Education (with Packt Publisher)
• Introduction to FinTech Using R | Video, link
• Fundamentals of Machine Learning | Video, link
• Fundamentals of Neural Networks | Video, link
• More to come.

Software Engineer: MLOps | LLMOps | DevOps | Full Stack

• MLOps Deck | Link | The slide deck walks through some basic points of becoming a good MLOps or LLMOps engineer.
• Software-as-a-Service (SAAS) Template | Link | The repository is hosted on HuggingFace and it walks you through a front-end User Interface (UI) in Streamlit application and a user authentication plugin.
• SAAS Chatbot Template | Link | The repository walks through the main components of building a web-based application with a Llama3 chatbot. The app is upgraded with user authentication and supported with a private API key.

List of AI and ML Textbooks

Collected Notes