Skip links

  • Skip to primary navigation
  • Skip to content
  • Skip to footer
  • Home
  • Projects
  • Notes
  • Reviews
  • Contact Me
    • Lecture 1
      • Supervised and Unsupervised Learning
      • Error Function
    • Lecture 2
      • Linear Regression
      • Least Square Solution
    • Lecture 3
      • Probabilistic Linear Regression
      • Overfitting and Regularization
    • Lecture 4
      • Univariate
      • Extend to Multivariate
    • Lecture 5
      • MAP and Bayes Estimates
      • Regularized Ridge Regression
    • Lecture 6
      • Iterative Soft Thresholding
      • Evaluating Performance
      • Bias Variance Analysis
    • Lecture 7
      • Perceptrons
      • Perceptron Algorithm
    • Lecture 8
      • Analysis of Perceptron Algorithm
      • Stochastic Gradient Descent
    • Lecture 9
      • Logistic Regression
      • Regularized Logistic Regression
      • Multiclass Logistic Regression
    • Lecture 10
      • Kernel Perceptrons
      • Gram Matrices
    • Lecture 11 - Kernelized Logistic Regression
    • Lecture 12
      • Dimensionality Reduction
      • Principal Component Analysis
      • Kernel PCA
    • Lecture 13
      • K-Means algorithm
      • Kernel K-Means
    • Lecture 14 - SVM
    • Lecture 15 - Neural Networks
      • Activation Functions
      • VC dimensions
    • Lecture 16 - Back Propogation
    • Lecture 17 - CNNs
    • Lecture 18 - Pooling in CNNs

    Artificial Intelligence and Machine Learning

    The notes of CS337 have been divided lecture-wise, and they can be found below; or accessed via the sidebar.

    • Lecture1
    • Lecture2
    • Lecture3
    • Lecture4
    • Lecture5
    • Lecture6
    • Lecture7
    • Lecture8
    • Lecture9
    • Lecture10
    • Lecture11
    • Lecture12
    • Lecture13
    • Lecture14
    • Lecture15
    • Lecture16
    • Lecture17
    • Lecture18
    • Follow:
    • GitHub
    © 2023 Akash Cherukuri. All Lefts Reserved.