Schedule
-
EventDateDescriptionCourse Material
-
Lecture07/27/2025
Sunday(foml-00) Course logistics[slides] -
Lecture07/28/2025
Monday(foml-01) ML and Learning Paradigms[slides]Suggested Reading
-
Lecture07/30/2025
Wednesday(foml-02) Probability Refresher - 1Suggested Reading
-
Lecture07/31/2025
Thursday(foml-03) Probability Refresher - 2Suggested Reading
-
Lecture08/06/2025
Wednesday(foml-04) MLESuggested Reading
-
Lecture08/07/2025
Thursday(foml-05) MAP and Fully Bayesian TreatmentSuggested Reading
-
Lecture08/11/2025
Monday(foml-06) Linear Regression with Basis functionsSuggested Reading
-
Lecture08/13/2025
Wednesday(foml-07) Geometric Interpretation for Least SquaresSuggested Reading
-
Lecture08/13/2025
Wednesday(foml-08) SGDSuggested Reading
-
Lecture08/14/2025
Thursday(foml-09) Regularized Least SqauresSuggested Reading
-
Lecture08/21/2025
Thursday(foml-10) Bias Variance DecompositionSuggested Reading
-
Lecture08/25/2025
Monday(foml-11) Bayesian RegressionSuggested Reading
-
Lecture08/25/2025
Monday(foml-12) Decision TheorySuggested Reading
-
Lecture09/03/2025
Wednesday(foml-13) Probabilistic Generative ModelsSuggested Reading
-
Lecture09/04/2025
Thursday(foml-14) Probabilistic Generative Models - discrete featuresSuggested Reading
-
Lecture09/04/2025
Thursday(foml-15) Discriminant FunctionsSuggested Reading
-
Lecture09/08/2025
Monday(foml-16) Discriminative Models - Least Squares RegressionSuggested Reading
-
Lecture09/08/2025
Monday(foml-17) Discriminative Models - The PerceptronSuggested Reading
-
Lecture09/15/2025
Monday(foml-18) Classification with Basis FunctionsSuggested Reading
-
Lecture09/15/2025
Monday(foml-19) Probabilistic Discriminative Models - Logistic RegressionSuggested Reading
-
Lecture09/17/2025
Wednesday(foml-20) Logistic Regression - SGDSuggested Reading
-
Lecture09/17/2025
Wednesday(foml-21) Logistic Regression - Newton Raphson Optimization (IRLS)Suggested Reading
-
Lecture09/18/2025
Thursday(foml-22) Neural Networks - ISuggested Reading
-
Lecture09/22/2025
Monday(foml-23) Neural Networks - IISuggested Reading
-
Lecture09/24/2025
Wednesday(foml-24) Neural Networks - IIISuggested Reading
-
Lecture10/06/2025
Monday(foml-25) Clustering - 1Suggested Reading
- Chapter 14 of Introduction to Statistical Learning textbook by Gareth James et al.
- Chapter 22 of Understanding ML: From Theory to Algorithms book by Shai Shalev-Shwartz et al.
- Chapter 9 of PR and ML book by Christopher M Bishop
- Chapter 7 of Introduction to Machine Learning by Ethem Alpaydın
- Chapter 25 of Machine Learning: a Probabilistic Perspective by Kevin Murphy
-
Lecture10/13/2025
Monday(foml-26) Clustering - 2Suggested Reading
- Chapter 14 of Introduction to Statistical Learning textbook by Gareth James et al.
- Chapter 22 of Understanding ML: From Theory to Algorithms book by Shai Shalev-Shwartz et al.
- Chapter 9 of PR and ML book by Christopher M Bishop
- Chapter 7 of Introduction to Machine Learning by Ethem Alpaydın
- Chapter 25 of Machine Learning: a Probabilistic Perspective by Kevin Murphy
-
Lecture10/13/2025
Monday(foml-27) Clustering - 3[slides] [source-slides]Suggested Reading
- Chapter 14 of Introduction to Statistical Learning textbook by Gareth James et al.
- Chapter 22 of Understanding ML: From Theory to Algorithms book by Shai Shalev-Shwartz et al.
- Chapter 9 of PR and ML book by Christopher M Bishop
- Chapter 7 of Introduction to Machine Learning by Ethem Alpaydın
- Chapter 25 of Machine Learning: a Probabilistic Perspective by Kevin Murphy
-
Lecture10/15/2025
Wednesday(foml-28) Latent Variable Models, GMM, and EM algorithmSuggested Reading
-
Lecture10/16/2025
Thursday(foml-29) Principcal Component Analysis - 1Suggested Reading
-
Lecture10/22/2025
Wednesday(foml-30) Principal Component Analysis - 2Suggested Reading
-
Lecture10/29/2025
Wednesday(foml-31) Kernelizing linear methodsSuggested Reading