CS6140 schedule

CS6140/4420 Machine Learning Section 9, Fall 2025

About CS6140 Home Schedule Piazza Project Gradescope

* Schedule and materials subject to change

Week / Module	Topic / Lecture	Other Reading	Assignment
9/4 - 9/11 Week 1 / Module 1: Intro, Regression Course Map Topics: Administrative Intro to ML, Matrix Data Linear Regression Setup, Cross Validation Error, Accuracy, ROC, AUC	Slides: Intro Screencast 05/07 Notes: Linear regression Slides Screencast 1 Lecture 1 Notes Screencast 2 Lecture 2 Notes Notes: Ridge Regression (normal equations)	Background: Probabilities Convexity1 Convexity2 Linear algebra Programming: MATLAB, Java, Python, R DHS 8.1-8.5 KMPP ch 16.2 DHS ch 5 (up to 5.4.2) KMPP ch 7 (up to 7.5) L1-regularization for Regression	HW 1 Due: 9/17
9/11 - 9/18 Week 2 / Module 1: Gradient Descent Course Map Topics: Gradient Descent Linear Regression with GD Logistic Regression Newton Method	Notes: Logistic Regression, Gradient Descent Slides: Logistic Regression, Newton UG-Book chapter : Logistic Regression Screencast 3: Gradient descent, logistic regression Lecture 3 Notes: Logistic Regression, Gradient Descent Screencast 4 Logistic Regression, Newton , ROC Lecture 4 Notes: Logistic Regression, Newton, ROC Screencast 5: Perceptron Lecture 5 Notes : Perceptron Notes: Perceptrons	DHS ch 5 KMPP ch 7, 8 Newton's Method Logistic regression trained with different loss functions
9/18 - 9/25 Week 3 / Module 2: SVM Course Map Topics: Support Vector Machines Duality with KKT conditions Maximizing Margins Constrained Optimization SMO Algorithm	Notes:Support Vector Machines Book Ch: Support Vector Machines Convex Optimization Slides: Constrained Optimization Lecture 5-6 : SVM (1,2) Screencast 5: Intro to SVM, Kernels (1) Screencast 6: SVM (2) Simplified SMO (Stanford) John Platt SMO	DSH ch 5.11 KMPP ch 14 Notes: Saddle-point Chris Burges SVM tutorial Wikipedia: SVM Notes (Columbia): SVM and VC dimension	HW2 Due: 10/1
9/25 - 10/2 Week 4 / Module 2: SVM, Kernels Course Map Topics: Kernels Kernels for SVM K-Nearest Neighbor Kernel Similarity and KNN Kernel Density Estimation Heat Kernels, Harmonic Equation	Recap: Distances and Similarities Kernels Kernels of other Algorithms, representer theorem Instance-based classification Local Classifiers Lecture 7 : kernels (1,2) Screencast 7: Kernels (2) Lecture 8 : kernels (3), KNN, t-SNE Screencast 8: KNN, Kernel-PCA Lecture 9 : PCA, KernelPCA	Notes (Andrew NG): SVM Slides: Kernel A primer on Kernel methods paper: Kernel Methods in Machine Learning Learning with harmonic functions
10/2 - 10/9 Week 5 / Module 3: Generative Course Map Topics: Probabilities as data densities Maximum Likelihood, fit params to data Gaussian Discriminant Analysis Naive Bayes	Probabilities Primer, Bayesian statistics Slides: Generative Intro Notes: GDA, Naive Bayes - Andrew Ng Slides: GDA Screencast 9: GDA Lecture 9: GDA Slides: Naive Bayes Smoothing Naive Bayes and Logistic Regression (CMU) Screencast 10: Naive Bayes, HW2B Lecture 10: Generative Classifiers, Naive Bayes	DHS ch 2, 3 KMPP ch 2, 3, 4 Wikipedia: Maximum Likelihood Wikipedia: Naive Bayes Wikipedia: Multivariate Normal Multivariate - marginal and conditional Wikipedia: Correlation Coefficient	HW 3 Due: 10/15
10/9 - 10/16 Week 6 / Module 3: Generative, EM Course Map Topics: EM algorithm for fitting mixtures Graphical Models	Notes: EM for Gaussian mixtures - Aron D'souza Slides (1): EM Slides (2): EM Screencast 11: GMM (1) Lecture 11+12: GMM (1+2) Screencast 12: GMM (2) Screencast 12: Mixture Binomial, Poisson (3) Lecture 13: Mixtures (3) Optional: Graphical Models / factor graphs Graphical models - Bishop slides	KMPP ch 20, KMPP ch 11(EM) EM code example Wikipedia: EM notes Notes (Berkeley): EM Notes (other): EM Slides (set 1): Other EM Graphical Models - Bishop chapter Graphical Models Bishop video lecture: Part 1, Part 2, Part 3
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	Notes: Decision trees Lecture 14 : Decision Trees Screencast 14 Other Decision Tree notes Boosting Slides: Adaboost Screencast 15: Boosting (1) Lecture 15 : Boosting (1+2+3) Slides: Gradient_boosting Example: Boosting Classifier Loss gradient	DHS ch 9 KMPP ch 16 Adaboost testing error proof Paper: Gradient Boosting VS Adaboost AdaBoost/Hedge Theory paper Boosting preferences - rankboost LambdaMart Slides, JForests Paper, implementation	HW 4 Due: 10/29
10/23 - 10/30 Week 8 / Module 4: Boosting, Features Course Map Topics: Active Learning and VC Dimension Multiclass ECOC	Screencast 16: Boosting (2), ECOC Lecture 16 : ECOC, Multiclass Notes: Active Learning Slides: Active Learning, Screencast Wikipedia: VC dimension Slides: VC dimension Slides:Multiclass ECOC Paper: Multiclass ECOC, Screencast	DHS ch 9.5 KMPP ch 16.6 KMPP ch 27.6.2 Wikipedia: Bagging
10/30 - 11/6 Week 9 / Module 4: Features, Margins Course Map Topics: Margins, Boosting Feature Analysis PCA and LDA, Lagrangian Multipliers Regularized Regression RIDGE and LASSO Missing Values	Screencast 17: Margins, PCA Lecture 17 : Margins, PCA Margins and Feature Analysis for Boosting (screencast) Feature selection. LASSO / RIDGE regularization for Regression (slides Haar Features for images - Wikipedia ( slides, Optional: Features for text: indexing, ngram matching Optional: CRFs for text 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 Article: Cool article on feature engineering Slides (Princeton): Feature Engineering Slides (Berkeley): Feature Engineering and Selection Regularized Logistic Regression Stanford slides regularized Logistic Regression-Andrew Ng (paper, screencast) Haar features integrated with boosting
11/6 - 11/13 Week 10 / Module 5: Neural Networks Course Map Topics: Perceptrons Neural Networks	Notes: Perceptrons, Neural Networks Screencast 18 Neural Networks (1) Lecture 18: NN Basics Book: Intro to Neural Networks Visualizer : Neural Networks Screencast 19 Neural Networks (2) Lecture 19: AutoEncoderNN, Word Vectors_NN, Activation Activation Function Table	DHS ch 6 Neural network toy example Other slides (Mitchell) Brilliant article on BackPropagation Original BackPropagation paper by NEU's Ron Williams	HW5 Due: 11/12
11/13-11/20 Week 11 / Module 5: Convolution Neural Networks			HW6 Due: 11/25
11/20 - 11/27 Week 12 / Module 6: Adv NNets Course Map Topics: Adv NN RNN TRN	Lecture 7/23 : Adv NN intro Screencast 7/23: t-SNE, Adv NN intro Lecture 7/25 : Convolution NNets (d2l slides w/ annotations) Screencast 7/23: Convolution NN basic CNN (d2l html+ pytorch) Modern CNN (d2l html+ pytorch)
11/27 - 12/4 Week 13 / Module 6: Adv NN Course Map Topics: TRN, attention	Lecture 7/30 : Recurrent NNets, LSTM, GRU (d2l slides w/ annotations) Screencast 7/30: Recurrent NN RNN (d2l html+ pytorch) Lecture 8/1 : RNN : Encoder-Decoder, Seqence-to-Sequence, Machine Translation (d2l slides w/ annotations) Screencast 8/1: RNN: GRU, LSTM, Encoder-Decoder for Seq-to-Seq RNN-Modern (d2l html+ pytorch)		HW7 Due: 12/10
12/4 - 12/11 Week 14 / Module 6: Adv NN Course Map Topics: TRN, attention	Lecture 8/6 : Attention Mechanic (d2l slides w/ annotations) Screencast 8/6: Attention Attention and Transformers (d2l html+ pytorch) Lecture 8/8 : Self Attention & Transformers (d2l slides w/ annotations) Screencast 8/6: Transformers Lecture 8/13 : NLP, Word-To-Vec, Glove Embedding (d2l slides w/ annotations) Screencast 8/13: NLP, Word Embedding, Glove NLP, Word Embedding (d2l html+ pytorch)
Tue Dec 9, WVH rooms 210-212 Exam 10:30 -2:30	Final Exam, you'll need a laptop. Expect programming problems similar to HW, about 1h to code and 15 min to run. You will have access to internet, but what you implement has to be your own. Submit a copy of your code on gradescope together with running instructions. The TAs are encouraging you to use Jupyter Notebook, but this is not a requirement for the exam.