# 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.

## 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.