Course Resources

(Last updated: Mar 20, 2023)

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

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

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

• I310D: Introduction to Human-Centered Data Science, University of Texas at Austin

• Human-Centered Data Science, FU Berlin and their course materials

• 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 Univewrsity

• 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

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 Cohen’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.

• Create Your First Book by Jupyter Book

• Data Science Study by Raphael Kim