Update week34.do.txt

doc/src/week34/week34.do.txt

@@ -14,9 +14,9 @@ lectures and questions and answers about the material to be covered
 every week. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm
 and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as
 possible for one of the groups. Max capacity per group is 30-40
-participants.
+participants. The labs are also available till 6pm Tuesdays and Wednesdays
 
-On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm.
+On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. 
 The first week we wtart with simple linear regression, a repetition of linear algebra and elements of statistics needed for the course. 
 
 
@@ -31,10 +31,11 @@ The first week we wtart with simple linear regression, a repetition of linear al
 
 !bblock
 For the reading assignments we use the following abbreviations:
-* GBC: Goodfellow, Bengio, and Courville, Deep Learning
+* GBC: "Goodfellow, Bengio, and Courville, Deep Learning":"https://www.deeplearningbook.org/"
 * CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
 * HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
 * AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
+* KM: "Kevin Murphy, Probabilistic Machine Learning":"https://probml.github.io/pml-book/book1.html"
 
 Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html (these notes).
 !eblock
@@ -667,9 +668,9 @@ The inverse of a matrix is defined by
 |----------------------------------------------------------------------|
 | $A=A^{T}$         | symmetric       | $a_{ij}=a_{ji}$            |
 | $A=\left (A^{T}\right )^{-1}$ | real orthogonal | $\sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj}=\delta_{ij}$ |
-| $A=A^{ * }$          | real matrix     | $a_{ij}=a_{ij}^{*}$       |
-| $A=A^{\dagger}$     |  hermitian      | $a_{ij}=a_{ji}^{*}$       |
-| $A=\left(A^{\dagger}\right )^{-1}$ | unitary | $\sum_k a_{ik}a_{jk}^{*}=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij}$ |
+| $A=A^*$          | real matrix     | $a_{ij}=a_{ij}^*$       |
+| $A=A^{\dagger}$     |  hermitian      | $a_{ij}=a_{ji}^*$       |
+| $A=\left(A^{\dagger}\right )^{-1}$ | unitary | $\sum_k a_{ik}a_{jk}^*=\sum_k a_{ki}^* a_{kj}=\delta_{ij}$ |
 |----------------------------------------------------------------------|
 
 !eblock
@@ -986,7 +987,7 @@ and many other operations.
 
 The _Series_ class is another important class included in
 _pandas_. You can view it as a specialization of _DataFrame_ but where
-we have just a single column of data. It shares many of the same features as _DataFrame. As with _DataFrame_,
+we have just a single column of data. It shares many of the same features as _DataFrame_. As with _DataFrame_,
 most operations are vectorized, achieving thereby a high performance when dealing with computations of arrays, in particular labeled arrays.
 As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in.
 For multidimensional arrays, we recommend strongly "xarray":"http://xarray.pydata.org/en/stable/". _xarray_ has much of the same flexibility as _pandas_, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both _pandas_ and _xarray_.