https://github.com/syedt1/machine-learning-notes
https://github.com/syedt1/machine-learning-notes
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/syedt1/machine-learning-notes
- Owner: SyedT1
- Created: 2022-03-02T14:12:48.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-03-31T19:48:46.000Z (about 3 years ago)
- Last Synced: 2025-07-25T09:58:30.572Z (11 months ago)
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Advanced Machine Learning Links
+ [**Reddit Link**](https://www.reddit.com/r/MachineLearning/comments/fdw0ax/d_advanced_courses_update/)
# Machine-Learning-Notes
## Students should have basic understanding of the following concepts as per mentioned by the course in the Intro of [this](https://www.youtube.com/watch?v=EfnJXeKmodw&list=PLcXJymqaE9PPGGtFsTNoDWKl-VNVX5d6b&index=1) playlist.
+ **Linear Algebra**
+ **Statistics**
+ **Random Variables**
+ **Stochastic Processes**
+ **Optimization for Static and Dynamic Systems**
+ **Image Processing**
## Supplements of this course
+ **Convex and Non-Convex Optimization**
+ **Convex Optimization - Stephen Boyd([Link to the website](https://web.stanford.edu/~boyd/cvxbook/))**
+ **Estimation Theory**
## Important Books to study to prepare notes of the lecture series [here](https://www.youtube.com/watch?v=EfnJXeKmodw&list=PLcXJymqaE9PPGGtFsTNoDWKl-VNVX5d6b&index=1) (which are highly recommended as well):
+ **Pattern Classification - Richard O' Duda**
+ **Statistical Pattern Recognition - Fukunaga**
+ **Machine Learning - A Probablistic Perspective by Kevin Murphy**
+ **Pattern Recognition and Machine Learning - Christopher M. Bishop**
+ **The Elements of Statistical Learning(Data Mining, Interference and Prediction) - Robert Tibshirani**
+ **A Probabilistic Theory of Pattern Recognition - Luc Devroye**
+ **Generative Methods**
+ **Principal Component Analysis-I.T.Jolliffe**
+ **Independent Component Analysis-Errkki Oja**
+ **Generative Methods for Classification**
+ **Discriminant Analysis and Statistical Pattern Recognition - Geoffrey J McLACHLAN**
+ **Clustering and Unsupervised Learning**
+ **Finite Mixture Models - Geoffrey J McLACHLAN**
+ **The EM Algorithm and Extensions- Geoffrey J McLACHLAN**
+ **Graphical Models**
+ **Probabilitistic Graphical Models - Principles and Techniques - DAPHNE KOLLER**
+ **Probabilitistic Reasoning in Intelligent Systems - Judea Pearl**
+ **Statistical Learning**
+ **Statistical Learning Theory - Vapnik**
+ **The Nature of the Statistical Learning Theory - Vapnik**
+ **Spline Models for observation of data - Grace Wahba**
+ **Learning from Data - Yaser S Abu Mustafa** and **his Lectures' playlist on Youtube**
+ **Kernel Methods for Pattern Analysis - John Shawe**
+ **Functional Data Analysis**
+ **Functional Data Analysis - J.O Ramsey**
+ **Deep Learning**
+ **Deep Learning - Ian GoodFellow**
+ **Combining Classifiers**
+ **Combining Pattern Classifiers - Ludmila Kuncheva**
+ **Some Other Books to Read for Understanding the content of the above book required for ML Topics**
+ **Vector Calculus - Anthony Tromba**
+ **Matrix Computations - Gene H Golub**
+ **[Introduction to Applied Linear Algebra - Vector, Matrices and Least Squares - Stephen Boyd](https://web.stanford.edu/~boy/vmls/)**
+ **Numerical Methods for unconstrained optimization and non linear equations - J.E Dennis Jr**
+ **Understanding the New Statistics - Geoff Cumming**
+ **Artificial Intelligence - A Modern Approach - Stuart Russell**
+ **Introduction to Algorithms - Thomas Cormen**
+ **General Reads:Related to What we're reading/learning from this course**
+ **Godel, Escher, Bach - An Eternal Golden Braid - Douglas R Hofstadter**
+ **The Theory of Games and Economic Behavior - Von Neumann**
+ **The Book of Why - Judea Pearl**
+ **The Society of Mind - Marvin Minsky**
+ **From Bacteria to Bach and Back - The evolution of minds - Daniel C. Dennett**
+ **Advice for a young investigator - Ramon y Cajal**