Advanced Machine Learning Links

• Reddit Link

Machine-Learning-Notes

Students should have basic understanding of the following concepts as per mentioned by the course in the Intro of this 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)
• Estimation Theory

Important Books to study to prepare notes of the lecture series here (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
  • 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