- Chapter 5 - Ensemble Methods
- Chapter 3 - Classification - corrections to proof of Hard-SVM and typos in Soft-SVM.
- Chapter 5 - code examples code updated.
- Updated environment.yml to include installation of
statsmodelsneeded for changes in chapter 5 code examples.
- Refactoring of code and adding explanations to Lab 05 - Logistic Regression
- Chapter 4 - PAC Theory of Statistical Learning
- Fixed indexing typos in Lab 04 - Polynomial Fitting.
- Chapter 3 - Classification.
- Chapter 2 - Bias and variance of estimators to linear regression.
- Chapter 1 - Convexity and second order approximations of functions.
- Refectoring of Chapter 3 code examples and move of AdaBoost example to chapter 5 code examples.
- Chapter 2 - Linear Regression
- Lab 02 - in last block, estimator is of the variance and not standard deviation.
- Minor typos and notation mistakes in chapter 1.
- Added definition of outer product.
- Added properties of SVD.
- Fixing dimensions inconsistency between Definition 1.2.4 and Exercise 1.5.
- Better explanations for the connection between SVD and EVD
- Fixed marginal of bivariate Gaussian proof.
- Fixed matrix transposing mistakes in 1.3.3.3
- Book title specifies chapters included in file instead of week of course.
- Minor fixes to Lab 01 - A - Data Analysis In Python - First Steps.
- Example 1.1 - Corrected number of linear equations and dimension of vector
$y$ . - Definition 1.1.9 - Removed non-negativity requirement of an inner product.
- SVD - Corrected dimensions of compact SVD form and explanations regarding lemma 1.1.9.
- Exercise 1.10 - Fixed confusing indices naming.
- Exercise 1.11 - Fixed errors in expressing
$f\left(\x\right)$ . - Definition 1.3.20 - Fixed matrix notation writing of sample covariance matrix estimator.