Adaptation and learning
EE-566
This file is part of the content downloaded from Adaptation and learning.
- Announcements (Forum)
- Q&A Forum (Forum)
- Syllabus for Spring 2025 (File)
- Background and Supporting Material (Text and media area)
- Video Lecture Recordings from Spring 2021 for your Convenience (URL)
- Review A: Matrix Theory (please read if you feel you need help on this topic) (File)
- Review B: Random Variables (please read if you feel you need help on this topic) (File)
- Review C: Gaussian Distribution (please read if you feel you need help on this topic) (File)
- HO1A: Vector Differentiation (File)
- HO1B: Convex Functions (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 1.1 (URL)
- Lecture 1.2 (URL)
- HO2A: Proximal Operator (File)
- HO2B: Gradient Descent Algorithms (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 2.1 (URL)
- Lecture 2.2 (URL)
- Solution for Homework 1 (File)
- HO3: Stochastic Optimization (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 3.1 (URL)
- Lecture 3.2 (URL)
- HO4A: Adaptive Gradient Methods (File)
- HO4B: Gradient Noise (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 4.1 (URL)
- Lecture 4.2 (URL)
- HO5A: Convergence Analysis (File)
- HO5B: Mean-Square-Error Inference (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 5.1 (URL)
- Lecture 5.2 (URL)
- Solution for Homework 2 (File)
- HO6: Bayesian Inference (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 6.1 (URL)
- Lecture 6.2 (URL)
- HO7A: Linear Regression (File)
- HO7B: Maximum Likelihood (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 7.1 (URL)
- Lecture 7.2 (URL)
- HO8A: Least-Squares Problems (File)
- HO8B: L2-Regularization (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 8.1: Least-Squares Problems (URL)
- Lecture 8.2: Regularization (URL)
- Solution for Homework 3 (File)
- HO9A: L1-Regularization (File)
- HO9B: Nearest-Neighbor Rule and K-means Clustering (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 9.1: L1-Regularization (URL)
- Lecture 9.2: Nearest-Neighbor Rule (URL)
- HO10A: Naïve Bayes Classifier (File)
- HO10B: Principal Component Analysis (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 10.1 (URL)
- Lecture 10.3 (URL)
- HO11A: Logistic Regression (File)
- HO11B: Perceptron and Support Vector Machines (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 11.1 (URL)
- Lecture 11.2 (URL)
- Lecture 11.3 (URL)
- Solutions for Homework 4 (File)
- HO12A: Kernel Methods (File)
- HO12B: Generalization Theory (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 12.1 (URL)
- Lecture 12.2 (URL)
- HO13A: Neural Networks (File)
- Lecture Recordings from Spring 2021 (Text and media area)
- Lecture 13.1 (URL)
- Lecture 13.2 (URL)