Introduction

·AI and Machine Learning
·Data Science Fundamentals

 Multi-Layer Perception

·Nonlinear Regression
·Back Propagation and SGD

  Loss Functions

·Minimum Squares, Bayesian, Information Theoretic Loses
·Regularization Methods

 Convolutional Neural Networks

·Convolution Operations
·AlexNet, VGG, ResNet, NIN, Inception

 Graph Neural Networks

·Graph vs Vector Neural Networks
·Graph Convolutional Networks

 Generative Autoencoder Networks

·Autoencoders
·Variational Autoencoders

 Recurrent and Recursive Neural Networks

·Hidden Markov Models, Conditional Random Fields
·RNN/LSTM,Transformer/BERT

 Sequence Search and Parsing

·Tree Structures and Conditional Random Fields
·Perception with Inexact Search,Transition-based Parsing.

 Transfer Learning and Domain Adaptation

·Inductive and Transductive Transfer Learning
·Unsupervised Transfer Learning

 Meta Imitation Learning

·One-Shot Learning, Model-Agnostic Meta-Learning
·Conjugated Task Graph

 Generative Adversarial Networks

·Generative vs. Discriminative Algorithms
·Basic GAN, Conditional GAN, CycleGAN, Adversarial AutoEncoder

 Reinforcement Learning

·Q-Learning, Policy Gradient
·Deep Q Network, Actor-Critic Algorithm

 Application: Computer Vision

·Person Re-Identification

 Application: Natural Language Processing

·Building a Chatbot

 Application: Robot Learning

·Visual SLAM

 Project Report Presentations