CS6140/4420 Machine Learning Section 9, Fall 2025

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* Schedule and materials subject to change
Week / Module Topic / Lecture Other Reading Assignment
  • 9/4 - 9/11
  • Topics:
  • Administrative
  • Intro to ML, Matrix Data
  • Linear Regression
  • Setup, Cross Validation
  • Error, Accuracy, ROC, AUC



Notes: Ridge Regression (normal equations)
  • 9/11 - 9/18
  • Topics:
  • Gradient Descent
  • Linear Regression with GD
  • Logistic Regression
  • Newton Method

  • DHS ch 5
  • KMPP ch 7, 8
  • 9/18 - 9/25
  •  
  • Topics:
  • Support Vector Machines
  • Duality with KKT conditions
  • Maximizing Margins Constrained Optimization
  • SMO Algorithm


  • DSH ch 5.11
  • KMPP ch 14
  • HW2
  • Due: 10/1
  • 9/25 - 10/2
  • Topics:
  • Kernels
  • Kernels for SVM
  • K-Nearest Neighbor
  • Kernel Similarity and KNN
  • Kernel Density Estimation
  • Heat Kernels, Harmonic Equation

paper: Kernel Methods in Machine Learning

  • Learning with harmonic functions

    • 10/2 - 10/9
    • Topics:
    • Probabilities as data densities
    • Maximum Likelihood, fit params to data
    • Gaussian Discriminant Analysis
    • Naive Bayes

    • DHS ch 2, 3
    • KMPP ch 2, 3, 4
    • 10/9 - 10/16
    • Topics:
    • EM algorithm for fitting mixtures
    • Graphical Models

    • 10/16 - 10/23
    • Week 7 / Module 4: Decision Trees, Boosting, Features
    • Course Map

    • Topics:
    • Online Learning
    • Rule-based Classifiers
    • Decision/Regression Trees
    • Adaboost Algorithm
    • Bagging
    • RankBoost, Gradient Boosting


    • 10/23 - 10/30
    • Week 8 / Module 4: Boosting, Features
    • Course Map

    • Topics:
    • Active Learning and VC Dimension
    • Multiclass ECOC


    • DHS ch 9.5
    • KMPP ch 16.6
    • KMPP ch 27.6.2
    • 10/30 - 11/6
    • Topics:
    • Margins, Boosting Feature Analysis
    • PCA and LDA, Lagrangian Multipliers
    • Regularized Regression RIDGE and LASSO
    • Missing Values
    PrincipalComponent Analysis (slides, sceencast)
    Missing Values and Naive Bayes

    optional: Fischer LDA
    Slides: tSNE / paper / implementation
    optional: tSNE gradient calculation
    • PCA: DHS ch 10
    • KMPP ch 25
    • 11/6 - 11/13
    • Topics:
    • Perceptrons
    • Neural Networks


    • DHS ch 6
    • HW5
    • Due: 11/12
    • 11/13-11/20

    • Week 11 / Module 5: Convolution Neural Networks
  • HW6
  • Due: 11/25
    		•
    • 11/20 - 11/27
    •    
    • Topics:
    • Adv NN
    • RNN
    • TRN


    • 11/27 - 12/4

    • Topics:
    • TRN, attention
    • HW7
    • Due: 12/10
    • 12/4 - 12/11

    • Topics:
    • TRN, attention