AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Stanford University via YouTube Video playlist
Stanford CS229: Machine Learning I Spring 2022
Overview
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
View the course website: https://cs229.stanford.edu/syllabus-spring2022.html
Videos in this playlist 19
- Stanford CS229 Machine Learning I Introduction I 2022 I Lecture 1
- Stanford CS229 Machine Learning I Supervised learning setup, LMS I 2022 I Lecture 2
- Stanford CS229 I Weighted Least Squares, Logistic regression, Newton's Method I 2022 I Lecture 3
- Stanford CS229 Machine Learning I Exponential family, Generalized Linear Models I 2022 I Lecture 4
- Stanford CS229 Machine Learning I Gaussian discriminant analysis, Naive Bayes I 2022 I Lecture 5
- Stanford CS229 Machine Learning I Naive Bayes, Laplace Smoothing I 2022 I Lecture 6
- Stanford CS229 Machine Learning I Kernels I 2022 I Lecture 7
- Stanford CS229 Machine Learning I Neural Networks 1 I 2022 I Lecture 8
- Stanford CS229 Machine Learning I Neural Networks 2 (backprop) I 2022 I Lecture 9
- Stanford CS229 Machine Learning I Bias - Variance, Regularization I 2022 I Lecture 10
- Stanford CS229 Machine Learning I Feature / Model selection, ML Advice I 2022 I Lecture 11
- Stanford CS229 I K-Means, GMM (non EM), Expectation Maximization I 2022 I Lecture 12
- Stanford CS229 Machine Learning I GMM (EM) I 2022 I Lecture 13
- Stanford CS229 Machine Learning I Factor Analysis/PCA I 2022 I Lecture 14
- Stanford CS229 Machine Learning I PCA/ICA I 2022 I Lecture 15
- Stanford CS229 Machine Learning I Self-supervised learning I 2022 I Lecture 16
- Stanford CS229 I Basic concepts in RL, Value iteration, Policy iteration I 2022 I Lecture 17
- Stanford CS229 I Societal impact of ML (Guest lecture by Prof. James Zou) I 2022 I Lecture 18
- Stanford CS229 Machine Learning I Model-based RL, Value function approximator I 2022 I Lecture 20
Advantages and disadvantages
Advantages
- Free and complete: every lecture is in the playlist, in order.
- Watch at 1.5x, skip what you know, rewatch what you don't.
- The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
- Taught by Stanford University, one of the strongest names in its field.
- 410,480 views, so help and notes are easy to find.
- Completely free.
- Self-paced: start any time.
- A clear syllabus (19 videos) you can see before you start.
Disadvantages
- No certificate, deadlines or graded work.
- Quality and depth vary: check that the playlist is finished before you start.
- Graduate level: needs linear algebra, probability and programming first.
- No certificate.
- No graded assignments or feedback.
Some points apply to every course of this kind; see how we rank.
Free
- Free: Watch on YouTube, no account needed.
- Certificate: None. Code or take notes along to make it stick.
Stanford University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (97/100).
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