Update README.md

README.md

@@ -39,23 +39,23 @@ by Joan Bruna, UC Berkeley, Statistics Department
 
 ## Schedule
 
-- **Lec1** Jan 19: Intro and Logistics
+- **[Lec1](lec1.pdf)** Jan 19: Intro and Logistics
 
-- **Lec2** Jan 21: Representations for Recognition : stability, variability. 
+- **[Lec2](lec2.pdf)** Jan 21: Representations for Recognition : stability, variability. 
  Kernel approaches / Feature extraction. Properties. 
 
   *recommended reading:*
   - [Elements of Statistical Learning, chapt. 12](http://statweb.stanford.edu/~tibs/ElemStatLearn/), Hastie, Tibshirani, Friedman.
   - [Understanding Deep Convolutional Networks](http://arxiv.org/pdf/1601.04920.pdf), S. Mallat.
 
-- **Lec3** Jan 26: Groups, Invariants and Filters.
+- **[Lec3](lec3.pdf)** Jan 26: Groups, Invariants and Filters.
 
   *recommended reading*
   - [Learning Stable Group Invariant Representations with Convolutional Networks](http://cims.nyu.edu/~bruna/Misc/iclr_group2.pdf)
   - [Understanding Deep Convolutional Networks](http://arxiv.org/pdf/1601.04920.pdf), S. Mallat.
   - [A Wavelet Tour of Signal Processing, chapt 2-5,7](https://www.ceremade.dauphine.fr/~peyre/wavelet-tour/), S. Mallat.
 
-- **Lec4** Jan 28: Scattering Convolutional Networks.
+- **[Lec4](lec4.pdf)** Jan 28: Scattering Convolutional Networks.
 
   *recommended reading*
   - [Invariant Scattering Convolutional Networks](http://arxiv.org/pdf/1203.1513v2.pdf)
@@ -64,18 +64,18 @@ by Joan Bruna, UC Berkeley, Statistics Department
   - [Group Invariant Scattering](http://arxiv.org/abs/1101.2286), S. Mallat
   - [Scattering Representations for Recognition](http://cims.nyu.edu/~bruna/PhD.html)
 
-- **Lec5** Feb 2: Further Scattering: Properties and Extensions.
+- **[Lec5](lec5.pdf)** Feb 2: Further Scattering: Properties and Extensions.
 
   *recommended reading*
   - [Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination](http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Sifre_Rotation_Scaling_and_2013_CVPR_paper.pdf), Sifre & Mallat.
 
-- **Lec6** Feb 4: Convolutional Neural Networks: Geometry and first Properties.
+- **[Lec6](lec6.pdf)** Feb 4: Convolutional Neural Networks: Geometry and first Properties.
 
   *recommended reading*
   - [Deep Learning](http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html) Y. LeCun, Bengio & Hinton.
   - [Understanding Deep Convolutional Networks](http://arxiv.org/pdf/1601.04920.pdf), S. Mallat.
 
-- **Lec7** Feb 9: Properties of learnt CNN representations: Covariance and Invariance, redundancy, invertibility
+- **[Lec7](lec7.pdf)** Feb 9: Properties of learnt CNN representations: Covariance and Invariance, redundancy, invertibility
 
   *recommended reading*
   - [Deep Neural Networks with Random Gaussian Weights: A universal Classification Strategy?](http://arxiv.org/abs/1504.08291), R. Giryes, G. Sapiro, A. Bronstein.
@@ -84,7 +84,7 @@ by Joan Bruna, UC Berkeley, Statistics Department
   - [Inverting Visual Representations with Convolutional Networks](http://arxiv.org/abs/1506.02753), A. Dosovitskiy, T. Brox.
   - [Visualizing and Understanding Convolutional Networks](http://arxiv.org/abs/1311.2901) M. Zeiler, R. Fergus.
 
-- **Lec8** Feb 11: Connections with other models (DL, Lista, Random Forests, CART) 
+- **[Lec8](lec8.pdf)** Feb 11: Connections with other models (DL, Lista, Random Forests, CART) 
 
  *recommended reading*
  - [Proximal Splitting Methods in Signal Processing](http://arxiv.org/pdf/0912.3522v4.pdf) Combettes & Pesquet.
@@ -95,7 +95,7 @@ by Joan Bruna, UC Berkeley, Statistics Department
  - [Improving the Fisher Kernel for Large-Scale Image Classification](https://www.robots.ox.ac.uk/~vgg/rg/papers/peronnin_etal_ECCV10.pdf) F. Perronnin et al.
  - [NetVLAD](http://www.di.ens.fr/willow/research/netvlad/) R. Arandjelovic et al.
 
-- **Lec9** Feb 16: Other high level tasks: localization, regression, embedding, inverse problems. 
+- **[Lec9](lec9.pdf)** Feb 16: Other high level tasks: localization, regression, embedding, inverse problems. 
 
  *recommended reading*
  - [Object Detection with Discriminatively Trained Deformable Parts Model](https://www.cs.berkeley.edu/~rbg/papers/Object-Detection-with-Discriminatively-Trained-Part-Based-Models--Felzenszwalb-Girshick-McAllester-Ramanan.pdf) Felzenswalb, Girshick, McAllester and Ramanan, PAMI'10
@@ -105,7 +105,7 @@ by Joan Bruna, UC Berkeley, Statistics Department
  - [Conditional Random Fields as Recurrent Neural Networks](http://arxiv.org/pdf/1502.03240.pdf) Zheng et al, ICCV'15
  - [Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation](http://arxiv.org/abs/1406.2984) Tompson, Jain, LeCun and Bregler, NIPS'14.
 
-- **Lec10** Feb 18:  Extensions to non-Euclidean domain. Representations of stationary processes. Properties. 
+- **[Lec10](lec10.pdf)** Feb 18:  Extensions to non-Euclidean domain. Representations of stationary processes. Properties. 
 
  *recommended reading*
  - [Dimensionality Reduction by Learning an Invariant Mapping](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) Hadsell, Chopra, LeCun,'06.