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CS229: Machine Learning. Instructor: Prof. Andrew Ng, Department of Computer Science, Stanford University.
555 years, 6 months
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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 robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)
Course Currilcum
- Lecture 01 – Introduction and Basic Concepts Unlimited
- Lecture 02 – Linear Regression and Gradient Descent Unlimited
- Lecture 03 – Locally Weighted and Logistic Regression Unlimited
- Lecture 04 – Perceptron and Generalized Linear Model Unlimited
- Lecture 05 – GDA and Naive Bayes Unlimited
- Lecture 06 – Support Vector Machines Unlimited
- Lecture 07 – Kernels Unlimited
- Lecture 08 – Data Splits, Models and Cross-Validation Unlimited
- Lecture 09 – Approx/Estimation Error and ERM Unlimited
- Lecture 10 – Decision Trees and Ensemble Methods Unlimited
- Lecture 11 – Introduction to Neural Networks Unlimited
- Lecture 12 – Backprop and Improving Neural Networks Unlimited
- Lecture 13 – Debugging ML Models and Error Analysis Unlimited
- Lecture 14 – Expectation-Maximization Algorithms Unlimited
- Lecture 15 – EM Algorithm and Factor Analysis Unlimited
- Lecture 16 – Independent Component Analysis and RL Unlimited
- Lecture 17 – MDPs and Value/Policy Iteration Unlimited
- Lecture 18 – Continuous State MDP and Model Simulation Unlimited
- Lecture 19 – Reward Model and Linear Dynamical System Unlimited
- Lecture 20 – RL Debugging and Diagnostics Unlimited
