# Course Resources

## Table of Contents

- PDF Books
- Web Books
- Machine Learning Courses
- Statistics Courses
- Data Science Courses
- Deep Learning Courses
- Computer Vision Courses
- Natural Language Processing Courses
- Reinforcement Learning Courses
- Human-Centered AI Courses
- Multimodal Learning Courses
- Videos
- Others

(Last updated: Jan 23, 2024)

This page curates a list of resources that are used or referenced in this course. These resources also inspired the development of this course.

This course shares similarities with the ML4Design course in TU Delft, and thus you can use the ML4Design lecture notes to self-study. The ML4Design course coordinator has agreed that I can use their notes as reference material in this course.

## PDF Books

Below is a list of books (in PDF form).

- The Elements of Statistical Learning and the PDF file
- Columbia University Applied Data Science and the PDF file
- Mathematics for Machine Learning and the PDF file
- Advanced Data Analysis from an Elementary Point of View and the PDF file
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science and the PDF file
- Machine Learning and the PDF file
- Neural Networks and Deep Learning and the PDF file
- Convex Optimization and the PDF file
- Pattern Recognition and Machine Learning and the PDF file
- Introduction to Statistics and Data Analysis and the PDF file
- Reinforcement Learning: An Introduction and the PDF file
- Speech and Language Processing and the PDF file
- Computer Vision: Algorithms and Applications and the PDF file downloading link
- Deep Reinforcement Learning and the PDF file downloading link

## Web Books

Below is a list of web books (in HTML form).

- Python Data Science Handbook
- Book of Human-Computer Interaction Concepts
- Think Bayes
- Deep Learning
- A Course in Machine Learning
- Hands-On Machine Learning with R
- R for Data Science
- Machine Learning and Deep Learning Fundamentals
- Dive into Deep Learning
- Introduction to Cultural Analytics & Python
- The Turing Way handbook
- Pro Git
- Flexible Imputation of Missing Data
- Handbook of Biological Statistics

## Machine Learning Courses

Below is a list of course notes and materials for machine learning.

- IOB4-T3: Machine Learning for Design in TU Delft
- CSE446: Machine Learning, University of Washington
- CS4780: Machine Learning for Intelligent Systems, Cornell University
- 10-601: Introduction to Machine Learning, Carnegie Mellon University
- 36-702: Statistical Machine Learning, Carnegie Mellon University
- ECE 595: Machine Learning, Purdue University
- Machine Learning with scikit-learn, Inria
- Cheatsheet for AI, machine learning, and deep learning courses, Stanford University
- CSC2541 Machine Learning: Neural Net Training Dynamics, University of Toronto
- CSC 311: Introduction to Machine Learning, University of Toronto
- Intro and Overview Machine Learning Lecture, Stuttgart Media University
- CIS 419/519 : Applied Machine Learning, University of Pennsylvania
- Introduction to Machine Learning, Google LLC
- Machine Learning from Scratch, Harvard Medical School
- APS360: Fundamentals of AI, University of Toronto

## Statistics Courses

Below is a list of course notes and materials for statistics.

- STAT 462: Applied Regression Analysis, Penn State
- STAT 500: Applied Statistics, Penn State
- STAT 800: Applied Research Methods, Penn State
- STAT 501: Regression Methods, Penn State
- STAT 508: Applied Data Mining and Statistical Learning

## Data Science Courses

Below is a list of course notes and materials for data science.

- Data 8: The Foundations of Data Science, UC Berkeley and its course note
- DSC 10: Principles of Data Science, UC San Diego and its course note
- Introduction to Research Data Science, The Alan Turing Institute
- Cheatsheet for data science tools, Massachusetts Institute of Technology
- COMP 5360: Introduction to Data Science, University of Utah and its GitHub

## Deep Learning Courses

Below is a list of course notes and materials for deep learning.

- Deep Learning Tutorials, University of Amsterdam
- DS-GA 1008: Deep Learning, NYU Center for Data Science
- 6.S191: Introduction to Deep Learning, Massachusetts Institute of Technology
- Intro to Deep Learning, Kaggle
- CPSC 532S: Multimodal Learning with Vision, Language and Sound, University of British Columbia

## Computer Vision Courses

Below is a list of course notes and materials for computer vision.

- CS231n: Deep Learning for Computer Vision, Stanford University
- CSE/ECE 576: Computer Vision, University of Washington
- CS5670: Introduction to Computer Vision, Cornell Tech
- 6.819/6.869: Advances in Computer Vision, Massachusetts Institute of Technology
- 16-385: Computer Vision, Carnegie Mellon University
- EECS 498.008 / 598.008: Deep Learning for Computer Vision, University of Michigan
- EECS 442: Computer Vision, University of Michigan
- CPSC 425: Computer Vision, University of British Columbia

## Natural Language Processing Courses

Below is a list of course notes and materials for natural language processing.

- CS224N: Natural Language Processing with Deep Learning, Stanford University
- CSE 447/517: Natural Language Processing, University of Washington

## Reinforcement Learning Courses

Below is a list of course notes and materials for reinforcement learning.

- COMP90054: Introduction to Reinforcement Learning, University of Melbourne
- CS 285: Deep Reinforcement Learning, University of California, Berkeley and their videos
- 10-703: Deep Reinforcement Learning, Carnegie Mellon University
- COMPM050: Reinforcement Learning, University College London

## Human-Centered AI Courses

Below is a list of course notes and materials for human-centered AI topics:

- CS 329X: Human-Centered NLP, Stanford University
- I310D: Introduction to Human-Centered Data Science, University of Texas at Austin
- Human-Centered Data Science, FU Berlin and their course materials
- 17-537: AI Methods for Social Good, Carnegie Mellon University

## Multimodal Learning Courses

Below is a list of course notes and materials for multimodal learning.

## Videos

Below is a list of online learning videos.

- Statistics and machine learning: StatQuest and their YouTube channel
- Animated math: 3Blue1Brown and their YouTube channel
- Mike X Cohenâ€™s YouTube channel that teaches math and statistics

## Others

Below is a list of other resources for self-learning.

- Machine Learning Resources
- Data Science Learning Resources
- Andrej Karpathy blog
- Christopher Olah blog
- Lilian Weng blog
- Hugging Face Documentations and Courses
- AI Summer: Learn Deep Learning and Artificial Intelligence
- Version Control with Git

Below is a list of resources that describe how to build a Jupyter Book.