## [Mar-Apr ‘19] Visual Recognition (Grad Level)

### Basic Info

**Where**: IIIT-Bangalore**When**: March/April 2019**Who**: Anush Sankaran, IBM Research AI (co-instructed with Prof. Dinesh Babu Jayagopi)

### Course Overview

Lecture | Topic | Content | Slides | Notes |
---|---|---|---|---|

1 | Introduction to Image Classification, Neural Networks, and Optimization | - What is visual recognition? - Logistic regression - Stochastic Gradient Descent - Multilayer perceptron - Backpropagation - DL + ML Pipeleine | slides | |

2 | Unsupervised Feature Learning, Autoencoders, Convolutional Neural Networks | - Popular applications of DL - Stacked autoencoders - Convolution & Pooling layers - Convolutional autoencoder | slides | Notebook |

3 | Hyper-parameter optimization, Training Process | Convolutional neural network - One time model setup - Hyper-parameter optimization | slides | Notebook |

4 | Different CNN Architectures | Data Augmentation - Transfer Learning - Comparison of Different CNN Architectures - Watson Studio Hands-on | slides | Watson Studio: How To |

5 | Generative Modelling | Unsupervised learning - Distribution fitting - PixelRNN/CNN - Variational Autoencoder (VAE) - Generative Adversarial Network (GAN) - Open source GAN toolkit | slides | Open source GAN Toolkit |

6 | CNN Visualization and Face Recognition | Neuron Visualization - Guided BackProp - Grad-CAM - Face Classification - Face Generation - DeepFake - Model Trust | slides |

### Acknowledgement

References and they have better slides! With huge respects to their slides, hard work, and efforts, I acknowledge them and only makes sense to reuse some part of their slides!

- Book on “Deep Learning” (https://www.deeplearningbook.org/ )
- CS231n: Convolutional Neural Networks for Visual Recognition (http://vision.stanford.edu/teaching/cs231n/index.html )
- CS 6501-004: Deep Learning for Visual Recognition (http://vicenteordonez.com/deeplearning/ )
- ECE 6504 Deep Learning for Perception (https://computing.ece.vt.edu/~f15ece6504/ )

## [Oct-Dec ‘19] Foundations of Machine Learning (MBA Students)

### Basic Info

**Where**: ISME-Bangalore**When**: October/ November 2019**Who**: Anush Sankaran, IBM Research AI

### Course Overview

Lecture | Topic | Slides | Notes |
---|---|---|---|

1 | Intro to ML, Discovering ML Use Cases & ML in Business | slides | |

2 | Python- Hands On, Supervised Learning & Regression | slides | Code Ex 1 Code Ex 2 |

3 | Neural Network - 1, Neural Network -2 & Hands ON | slides | Code Ex 3 Code Ex 4 |

4 | Kernel Learning & SVM, Practical Advice for ML projects. | slides | |

5 | Boosting, Decision Trees, Random Forest, & xgBoost | slides | |

6 | Unsupervised Learning, Clustering & Dimensionality Reduction | slides | |

7 | Time Series Data Analysis, Imputation & Prediction Systems | slides | |

8 | ML Use Cases from Products & Research | slides |

### Acknowledgement

Multiple references are borrowed from different sources of internet and different other courses, and they have better slides! With huge respects to their slides, hard work, and efforts, I acknowledge them and only makes sense to reuse some part of their slides!