diff --git a/doc/pub/week48/html/._week48-bs000.html b/doc/pub/week48/html/._week48-bs000.html index 82010ac12..20bc14f85 100644 --- a/doc/pub/week48/html/._week48-bs000.html +++ b/doc/pub/week48/html/._week48-bs000.html @@ -284,7 +284,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Nov 26, 2020

+

Nov 27, 2020


diff --git a/doc/pub/week48/html/._week48-bs001.html b/doc/pub/week48/html/._week48-bs001.html index 10f030a96..505be5281 100644 --- a/doc/pub/week48/html/._week48-bs001.html +++ b/doc/pub/week48/html/._week48-bs001.html @@ -268,8 +268,8 @@ MathJax.Hub.Config({

Overview of week 48

Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion. diff --git a/doc/pub/week48/html/._week48-bs012.html b/doc/pub/week48/html/._week48-bs012.html index 6b07facca..3917e8304 100644 --- a/doc/pub/week48/html/._week48-bs012.html +++ b/doc/pub/week48/html/._week48-bs012.html @@ -272,7 +272,7 @@ If we use Python as programming language and wish to venture beyond scikit-learn, tensorflow and similar software which makes our lives so much easier, we need to dive into the wonderful world of quadratic programming. We can, if we wish, solve the minimization -problem using say standard gradient methods or conjugate gradient +problem using standard gradient methods or conjugate gradient methods. However, these methods tend to exhibit a rather slow converge. So, welcome to the promised land of quadratic programming. diff --git a/doc/pub/week48/html/._week48-bs015.html b/doc/pub/week48/html/._week48-bs015.html index b2d596fa8..669c281b9 100644 --- a/doc/pub/week48/html/._week48-bs015.html +++ b/doc/pub/week48/html/._week48-bs015.html @@ -280,7 +280,7 @@ $$
  1. With a given kernel we can thus define the matrix \( \boldsymbol{P} \).
  2. The matrix \( \boldsymbol{P} \) has matrix elements \( p_{ij}=y_iy_jK(\boldsymbol{x}_i,\boldsymbol{x}_j) \). Given a kernel \( K \) and the targets \( y_i \) this matrix is easy to set up.
    3. -
  3. The vector \( \boldsymbol{q} \) has all elements equal -1.
    4. +
  4. The vector \( \boldsymbol{q} \) has all elements equal to \( -1 \).
  5. The constraint \( \boldsymbol{y}^T\boldsymbol{\lambda}=0 \) leads to \( f=0 \) and \( \boldsymbol{A}=\boldsymbol{y} \).
  6. To set up the matrix \( \boldsymbol{G} \) we note that the inequalities \( 0\leq \lambda_i \leq C \) can be split up into \( 0\leq \lambda_i \) and \( \lambda_i \leq C \). These two inequalities define then the matrix \( \boldsymbol{G} \) and the vector \( \boldsymbol{h} \).
diff --git a/doc/pub/week48/html/._week48-bs021.html b/doc/pub/week48/html/._week48-bs021.html index 04458f0b1..21312ef7a 100644 --- a/doc/pub/week48/html/._week48-bs021.html +++ b/doc/pub/week48/html/._week48-bs021.html @@ -268,16 +268,16 @@ MathJax.Hub.Config({

Statistical analysis and optimization of data

-The following topics be covered +The following topics have been discussed:

  1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;
  2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
    3. -
  3. Central elements from linear algebra
    4. +
  4. Central elements from linear algebra, matrix inversion and SVD
  5. Gradient methods for data optimization
  6. Estimation of errors using cross-validation, bootstrapping and jackknife methods;
  7. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
    8. -
  8. Principal Component Analysis.
    9. +
  9. Principal Component Analysis to reduce the number of features.

diff --git a/doc/pub/week48/html/._week48-bs023.html b/doc/pub/week48/html/._week48-bs023.html index b1e0310b1..df503077e 100644 --- a/doc/pub/week48/html/._week48-bs023.html +++ b/doc/pub/week48/html/._week48-bs023.html @@ -281,7 +281,7 @@ ethical conduct is emphasized throughout the course.