navigation

Advanced Predictive Models and Applications for Business Analytics (IDS576)

The goal of this class is to cover a subset of advanced machine learning techniques, after students have seen the basics of data mining (such as in in IDS 572) and machine learning (such as in IDS 575).

Broadly, we will cover topics spanning deep learning and reinforcement learning. In particular, we will study popular deep learning architectures, their design choices and how they are trained. This will be motivated by business applications dealing with image, text and tabular data. Finally, we will look at online and reinforcement learning frameworks and their role in sequential decision making settings such as retail.

A tentative list of topics is as follows:

  • Backpropagation and feed forward neural networks
  • Convolutional networks (CNNs)
  • Recurrent networks and Long short-term memory networks (LSTMs)
  • Attention mechanism and transformers (BERT, GPT-3)
  • Variational autoencoders (VAEs)
  • Generative adversarial networks (GANs)
  • Deep reinforcement learning and Deep Q-Network (DQN)
  • Deep learning design choices such as optimizers (Adam, RMSprop), nonlinearities, embeddings, attention, dropout, batch normalization etc.

See the playlist on Youtube for the recorded lectures. These were recorded in Fall 2020.

Previous Editions