• Course Code: 01:198:462
• Title: Introduction to Deep Learning
• Short Description: A comprehensive artificial intelligence system needs to not only perceive the environment with different “senses” (e.g., seeing and hearing) but also infer the world’s conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks, such as visual object recognition and speech recognition, using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning (BDL) has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models. In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and, in turn, the feedback from the inference process is able to enhance the perception of text or images. In this course, we will study the state-of-the-art methodology and theory in this emerging area, as well as applications including recommender systems, computer vision, natural language processing, graph learning, forecasting, healthcare, domain adaptation, speech recognition, etc.
• Degree Type (BA/BS): BS in Data Science
• Track Option: Computer Science Track (Code: NB219SJ)
• Degree Option: BS in Data Science- Computer Science Track (Code: NB219SJ)
• Prerequisite: M 250 (Introductory Linear Algebra), CS 112 (Data Structures), CS 206 (Introduction to Discrete Structures II) OR M 477 (Mathematical Theory of Probability) OR S 379 (Basic Probability Theory). CS 440 or CS 439 is not required but highly encouraged. Students must be familiar with python programming. The homework will be based on PyTorch, a python framework for deep learning. Thus, some experience of PyTorch will be helpful although the course will provide short intro lecture on using PyTorch. Students should set up the PyTorch programming environment as well. You could use your only computer, Rutgers iLab, or Google Colab to run the code.