A course for ML and Deep Learning Enthusiats During the COVID-19 pandemic, a team of volunteers comprising professors, industry professionals, and students working in data science, machine learning, deep learning and artificial intelligence have gotten together to offer a course to students on these topics.
Data Science (DS): Getting started, Basic data understanding, Improving plots, Basic statistics.
Machine Learning (ML): Introduction to ML, Decision trees, Bayesian decision theory, Linear models, Kernelization, Feature selection and engineering, Dense and shallow neural networks, Advanced topics in neural networks, Clustering, Model Explainability.
Deep Learning (DL) for Vision: Introduction to CNNs, Advanced conv nets, Semantic segmentation, Object detection, Instance segmentation, Few-shot learning, Metric learning, Generative Adversarial Networks (GANs), Variational Autoencoder (VAE).
Deep Learning for Natural Language Processing (NLP): Word embeddings, Language modeling, Simple applications of LSTMs, Advanced applications of LSTMs - 1, Relationship extraction, Advanced applications of LSTMs - 2, Advanced applications of LSTMs - 3, Beyond simple word embeddings, Beyond LSTMs, Chat-bot making.
Miscellaneous: Graph conv nets for NLP, Knowledge graphs, Reinforcement Learning.
Practical Implementation of ML Models: Deployment - 1, Deployment - 2, Deployment - 3, Front-end and logistics, Making APIs, Winning Kaggle, Compute and bandwith considerations, When to use which framework.