EE278: Previous Notes

Spring 2026 Notes (Supplemental Material)

Lecture notes by Abhiram Gorle from the Spring 2026 offering of EE278, taught by Tsachy Weissman. These are supplemental material for Fall 2026; the topics and their order may differ from this quarter's lectures.

Spring 2026 lecture notes (PDF)

Fall 2024 Notes (Supplemental Material)

These notes are from the Fall 2024 offering of EE278. They are supplemental material for Fall 2026, and may not cover all of the content discussed in lectures.

A latexed version of older lecture notes: Google Drive link

Handwritten Notes by Topic

• Inference Overview: Hypothesis Testing, MMSE Estimation, and Machine Learning

• Probability Review: Markov and Chebyshev Inequalities; LLN and CLT

• Concentration Inequalities; Hoeffding's Inequality; Moment Generating Functions

• Sub-Gaussian Random Variables; Hoeffding and McDiarmid Inequalities

• Machine Learning; Empirical Risk Minimization; Overfitting and Generalization

• Generalization Bounds; Uniform Convergence; Sample Complexity and Excess Risk

• Random Vectors; Means and Covariance Matrices; Linear Transformations

• Covariance Matrices; Spectral Decomposition; Karhunen-Loeve Expansion and Optimal Linear Approximation

• Principal Component Analysis; Positive Semidefinite Covariance Matrices; Whitening and Coloring

• Gaussian Random Vectors; Densities, Linear Transformations, and Independence

• Gaussian Density Geometry; Hypothesis Testing; MAP and ML

• Poisson and Scalar Gaussian Hypothesis Testing; Decision Thresholds and Error Probability

• Scalar and Vector Gaussian Hypothesis Testing; Minimum-Distance Decoding; Sufficient Statistics; Error Probability

• MMSE Estimation; Conditional Expectation; MMSE Geometry; Gaussian Conditioning

• Gaussian MMSE Estimation; Scalar and Vector Observations

• Sufficient Statistics; Recursive Estimation; Kalman Filtering

• Kalman Filtering; Introduction to Random and Gaussian Processes; Mean and Covariance Functions