AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Stanford University via YouTube Video playlist
Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018
Overview
Led by Andrew Ng, 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.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Videos in this playlist 21
- Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)
- Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)
- Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)
- Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
- Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
- Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
- Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018)
- Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018
- Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018
- Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018)
- Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018)
- Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018)
- RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018)
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.
- 4,852,594 views, so help and notes are easy to find.
- Completely free.
- Self-paced: start any time.
- A clear syllabus (21 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.
- Recorded in 2019: tools may have changed since.
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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