COURSE DESCRIPTION
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.
Course Features
- Lectures 19
- Quiz 0
- Duration 60 hours
- Skill level All levels
- Language English
- Students 33
- Assessments Yes
Curriculum
- 1 Section
- 19 Lessons
- 10 Weeks
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- Machine Learning I19
- 1.1Stanford CS229 Machine Learning I Introduction I 2022 I Lecture 1
- 1.2Stanford CS229 Machine Learning I Supervised learning setup, LMS I 2022 I Lecture 2
- 1.3Stanford CS229 I Weighted Least Squares, Logistic regression, Newton’s Method I 2022 I Lecture 3
- 1.4Stanford CS229 Machine Learning I Exponential family, Generalized Linear Models I 2022 I Lecture 4
- 1.5Stanford CS229 Machine Learning I Gaussian discriminant analysis, Naive Bayes I 2022 I Lecture 5
- 1.6Stanford CS229 Machine Learning I Naive Bayes, Laplace Smoothing I 2022 I Lecture 6
- 1.7Stanford CS229 Machine Learning I Kernels I 2022 I Lecture 7
- 1.8Stanford CS229 Machine Learning I Neural Networks 1 I 2022 I Lecture 8
- 1.9Stanford CS229 Machine Learning I Neural Networks 2 (backprop) I 2022 I Lecture 9
- 1.10Stanford CS229 Machine Learning I Bias – Variance, Regularization I 2022 I Lecture 10
- 1.11Stanford CS229 Machine Learning I Feature / Model selection, ML Advice I 2022 I Lecture 11
- 1.12Stanford CS229 I K-Means, GMM (non EM), Expectation Maximization I 2022 I Lecture 12
- 1.13Stanford CS229 Machine Learning I GMM (EM) I 2022 I Lecture 13
- 1.14Stanford CS229 Machine Learning I Factor Analysis/PCA I 2022 I Lecture 14
- 1.15Stanford CS229 Machine Learning I PCA/ICA I 2022 I Lecture 15
- 1.16Stanford CS229 Machine Learning I Self-supervised learning I 2022 I Lecture 16
- 1.17Stanford CS229 I Basic concepts in RL, Value iteration, Policy iteration I 2022 I Lecture 17
- 1.18Stanford CS229 I Societal impact of ML (Guest lecture by Prof. James Zou) I 2022 I Lecture 18
- 1.19Stanford CS229 Machine Learning I Model-based RL, Value function approximator I 2022 I Lecture 20
