Learning AI

My resources and messy learning path to understand this complex field better

20 Sep 2022

Branching out from !learning/computer-science for a more AI-focused learning path.

Not sure if I need to split into AI categories, ie NLP, machine learning, etc..? 🤔

Lists of AI courses

Of interest could be:

Artificial Intelligence

CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW
CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2015
6.034 Artificial Intelligence, MIT OCW
CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University
15-780 - Graduate Artificial Intelligence, Spring 14, CMU
CSE 592 Applications of Artificial Intelligence, Winter 2003 - University of Washington
CS322 - Introduction to Artificial Intelligence, Winter 2012-13 - UBC (YouTube)
CS 4804: Introduction to Artificial Intelligence, Fall 2016
CS 5804: Introduction to Artificial Intelligence, Spring 2015
Artificial Intelligence - IIT Kharagpur
Artificial Intelligence - IIT Madras
Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur
MOOC - Intro to Artificial Intelligence - Udacity
MOOC - Artificial Intelligence for Robotics - Udacity
Graduate Course in Artificial Intelligence, Autumn 2012 - University of Washington
Agent-Based Systems 2015/16- University of Edinburgh
Informatics 2D - Reasoning and Agents 2014/15- University of Edinburgh
Artificial Intelligence - Hochschule Ravensburg-Weingarten
Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany
Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
Semantic Web Technologies by Dr. Harald Sack - HPI
Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack - HPI
T81-558: Applications of Deep Neural Networks by Jeff Heaton, 2022, Washington University in St. Louis

Machine Learning

Introduction to Machine Learning
MOOC Machine Learning Andrew Ng - Coursera/Stanford (Notes)
Introduction to Machine Learning for Coders
MOOC - Statistical Learning, Stanford University
Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
CS155 - Machine Learning & Data Mining, 2017 - Caltech (Notes) (2016)
CS 156 - Learning from Data, Caltech
10-601 - Introduction to Machine Learning (MS) - Tom Mitchell - 2015, CMU (YouTube)
10-601 Machine Learning | CMU | Fall 2017
10-701 - Introduction to Machine Learning (PhD) - Tom Mitchell, Spring 2011, CMU (Fall 2014) (Spring 2015 by Alex Smola)
10 - 301/601 - Introduction to Machine Learning - Spring 2020 - CMU
CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech
Microsoft Research - Machine Learning Course
CS 446 - Machine Learning, Spring 2019, UIUC (Fall 2016 Lectures)
undergraduate machine learning at UBC 2012, Nando de Freitas
CS 229 - Machine Learning - Stanford University (Autumn 2018)
CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
CPSC 340: Machine Learning and Data Mining (2018) - UBC
CS4780/5780 Machine Learning, Fall 2013 - Cornell University
CS4780/5780 Machine Learning, Fall 2018 - Cornell University (Youtube)
CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo
CS 5350/6350 - Machine Learning, Fall 2016, University of Utah
ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
STAT 441/841 Classification Winter 2017 , Waterloo
10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
Python and machine learning - Stanford Crowd Course Initiative
MOOC - Machine Learning Part 1a - Udacity/Georgia Tech (Part 1b Part 2 Part 3)
Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
Introductory Applied Machine Learning 2015/16- University of Edinburgh
Pattern Recognition Class (2012)- Universität Heidelberg
Introduction to Machine Learning and Pattern Recognition - CBCSL OSU
Introduction to Machine Learning - IIT Kharagpur
Introduction to Machine Learning - IIT Madras
Pattern Recognition - IISC Bangalore
Pattern Recognition and Application - IIT Kharagpur
Pattern Recognition - IIT Madras
Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
Machine Learning - Professor Kogan (Spring 2016) - Rutgers
CS273a: Introduction to Machine Learning (YouTube)
Machine Learning Crash Course 2015
COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
10715 Advanced Introduction to Machine Learning
Introduction to Machine Learning - Spring 2018 - ETH Zurich
Machine Learning - Pedro Domingos- University of Washington
Advanced Machine Learning - 2019 - ETH Zürich
Machine Learning (COMP09012)
Probabilistic Machine Learning 2020 - University of Tübingen
Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen
COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University
Machine Learning for Engineers 2022 (YouTube)
10-418 / 10-618 (Fall 2019) Machine Learning for Structured Data

Natural Language Processing

CS 224N - Deep Learning for Natural Language Processing, Stanford University (Lectures - Youtube)
CS 224N - Natural Language Processing, Stanford University (Lecture videos)
CS 124 - From Languages to Information - Stanford University
MOOC - Natural Language Processing, Dan Jurafsky & Chris Manning - Coursera
fast Code-First Intro to Natural Language Processing (Github)
MOOC - Natural Language Processing - Coursera, University of Michigan
CS224U: Natural Language Understanding - Spring 2019 - Stanford University
Deep Learning for Natural Language Processing, 2017 - Oxford University
Accelerated Natural Language Processing 2015/16- University of Edinburgh
Natural Language Processing - IIT Bombay
CMU Advanced NLP 2021
CMU Neural Nets for NLP 2021
Natural Language Processing - Michael Collins - Columbia University
CMU CS11-737 - Multilingual Natural Language Processing
UMass CS685: Advanced Natural Language Processing (Spring 2022)
Natural Language Processing (CMSC 470)

Andrew Ng

Topics addressed

Course is comprised of short videos covering:

Machine Learning  
What is data  
The terminology of AI  
What makes an AI company?  
What Machine Learning can and cannot do  
Intuitive explanation of deep learning  
Workflow of a Machine Learning project  
Workflow of a Data Science project  
Every job function needs to learn to use data  
How to choose an AI project  
Working with an AI team  
Technical tools for AI teams  
Case study: Smart speaker  
Case study: Self-driving car  
Example roles of an AI team  
AI Transformation Playbook  
AI pitfalls to avoid  
Taking your first step in AI  
Survey of major AI applications  
Survey of major AI techniques  
A realistic view of AI  
Discrimination / Bias  
Adversarial attacks  
Adverse uses  
AI and developing nations  
AI and jobs  

Good start for a lot of people, too high-level for me at this stage.


Harvard's CS50’s Introduction to Artificial Intelligence with Python

➤ highly recommend


➤ highly recommend

other videos from Lex Fridman:


Found via:

Google Developers

1) Introduction to machine learning

  • get started with ML
  • Learn about supervised ML

2) machine learning crash course

  • How ML differs from traditional programming
  • How does gradient descent work?
  • End-to-end ML engineering
  • Production ML systems and more..

3) Introduction to ML problem framing

  • Understand how to frame the ML problem

4) Data preparation and FE in ML

  • Data preparation
  • Data transformation
  • Feature engineering

5) testing and debugging ML models

  • Train and test ML models
  • Debugging the ML models


Andrej Karpathy

a series of videos on Youtube - "It's absolutely the best introduction to neural networks I have seen." @krzysztofwos on Twitter.


Learning path

26 Oct 2022

Learning AI is a bit overwhelming 😅

I have done several courses like:

I have experience with Python (for automation mainly) and have played with Stable Diffusion via CLI and other models via online interfaces.

I feel that I'm missing education in between - mid level :)

Open questions

examples of missing connections in my mind at this stage:

  • what are all the different AI fields and how do they interact all together? eg are computer vision and NLP different fields with no AI overlap but leveraging the same methods (supervised/unsupervised/deep) and tools (Tensorflow, etc..)?
  • what defines an AI model? algorithm? eg is it only about changing the training set and weight parameters for specific criteria/neurons?
  • how does it look like? Just a Python library? With or without dataset included?
  • with deep learning, is it just running each time an input from a given dataset, ie train and ouput in same run, or does a computed algorithm get generated off the training dataset?
  • what are the different kinds of tools required to create an AI model? And to implement AI? And how do they interact?
  • what are the different kind of AI platforms and what do they do? Only providing GPUs in the cloud and a library of models/datasets? eg what is the difference between huggingface.co and c3.ai? Same thing but for consumers (ie developers) and enterprises?
  • how do you use an existing model in production (ie for real-life usages)? Via API?
  • how do you feedback results to an AI model for it to learn further?
  • my brain hurts from the complexity, will an AGI also experience brainaches? :)

What would be the resources to best advance my learning at this stage?

If helpful for guidance, my main practical interest/use of AI is for Sales/business (so probably focus on NLP?), though my curiosity is wider :)