Suggested Readings for the course

This list will be updated regularly. Read most or all of them to obtain broad understanding of the field. Be sure you have read something before coming to lectures so you have something in mind for discussion and brainstorming.

To see the latest research topics in Pattern Recognition, please check these journal or conference papers

  1. ICPR - International Conference on Pattern Recognition
  2. CVPR
  3. ICLR
  4. NIPS - Neural Information Processing Systems Conference
  5. Top Journals in Pattern recognition and machine learning
  6. Deep learning lecture slides
  7. Keras deep learning tutorial

Weekly Reading Materials

Week 1

What is pattern recognition? by Wikipedia

Learning feed-forward one-shot learners

current trends for feature learning in the unsupervised way

Deep Learning breakthrough history

Week 2

Machine learning step by step blog

Matrix Factorization SVM NMF etc

Matrix_decomposition

Week3

Recurrent Neural Networks

Matrix factorization tutorial with python

Week4

One-shot-learning

Week5

GAN:generative Adversarial Networks

EM-MLE-ML.pdf Supervised Learning from Multiple Experts

Excellent Tutorials of ICML 2009 conference with downloadable slides

Week6

Image super resolution via deep learning

Facebook posts its fast and accurate ConvNet models for machine translation

Week7

Deep learning for satellite imagery via image segmentation

Week8

U-Net: Convolutional Networks for Biomedical Image Segmentation

Week9

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

Week10

Deep Residual Learning for Image Recognition

Week11

HMM-stock.pdf stock market prediction using HMM

Week12

Densely Connected Convolutional Networks

Week13

Information Theory of Deep Learning. Naftali Tishby

Week14

Demystifying Deep Reinforcement Learning

Week15

MIT self-driving car research blog

Week16

Learning to learn