├── The AI Conference 2017.pdf ├── ReinforcementLearningCourse.pdf ├── ScaledMLConference2017Notes.pdf ├── ElementsofStatisticalLearningNotes.pdf ├── MachineLearningAProbabilisticPerspectiveNotes.pdf └── README.md /The AI Conference 2017.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adeshpande3/Machine-Learning-Notes/HEAD/The AI Conference 2017.pdf -------------------------------------------------------------------------------- /ReinforcementLearningCourse.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adeshpande3/Machine-Learning-Notes/HEAD/ReinforcementLearningCourse.pdf -------------------------------------------------------------------------------- /ScaledMLConference2017Notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adeshpande3/Machine-Learning-Notes/HEAD/ScaledMLConference2017Notes.pdf -------------------------------------------------------------------------------- /ElementsofStatisticalLearningNotes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adeshpande3/Machine-Learning-Notes/HEAD/ElementsofStatisticalLearningNotes.pdf -------------------------------------------------------------------------------- /MachineLearningAProbabilisticPerspectiveNotes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adeshpande3/Machine-Learning-Notes/HEAD/MachineLearningAProbabilisticPerspectiveNotes.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Notes 2 | Notes for several Machine Learning and Deep Learning courses and textbooks and talks. 3 | 4 | * **Reinforcement Learning Course** - This lecture course is taught by David Silver of Google Deepmind. The lectures can be found on YouTube at this [link](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT). In this course, you'll learn about the following topics. 5 | - Markov Decision Processes 6 | - Value functions 7 | - Policies 8 | - Dynamic programming approaches 9 | - Monte Carlo learning 10 | - Temporal difference learning 11 | - SARSA 12 | - Value function approximation 13 | - Policy gradients 14 | - Deep Q networks 15 | 16 | * **Scaled Machine Learning Conference Notes** - These were the notes I took at the Scaled ML conference at Stanford. Here is a link to [event page](http://scaledml.org/). 17 | 18 | * **Machine Learning: A Probabilistic Perspective Notes** - ML textbook with an emphasis on describing concepts with relation to probability. 19 | 20 | * **Elements of Statistical Learning Notes** - Another hard ML textbook with an emphasis on a lot of the math behind traditional ML models. 21 | 22 | * **The AI Conference 2017 Notes** - These were the notes I took at The AI Conference sponsored by O'Reilly Media. Here is a link to [event page](https://conferences.oreilly.com/artificial-intelligence/ai-ca). 23 | --------------------------------------------------------------------------------