In this course, we will not strictly follow predefined resources; instead, we will weekly provide slides, online assignments, and tutorials. We are aiming at presenting the topic for each week in a self-contained way. However, we believe that the resources provide on this page will enable students to explore more in the areas of multimodal machine learning and humanoid robotics.


There is no required textbook for this course. We recommend several freely available books to provide mathematical formalization and details.

Python/Numpy/Jupyter Notebook/TensorFlow tutorials

In this course, we will use Python 3 to show code snippets and prepare tutorials. We will also introduce TensorFlow 2.x and Keras to build machine learning models. You can also use other machine learning libraries: PyTorch, Caffee, Jax, etc. If you have programming experience with different languages and would like to switch to Python, the below list can be helpful to shorten the transition period.

Simulation environments for robotics

The main application area of this course is humanoid robotic. You can find free simulation environments for robotics in the following links:

Miscellaneous reading list

Resources for the assignments


The content of this course was inspired from various online courses on deep learning, computer vision and robotics including cs231, CSCI 1430, STAT 453, and EECS 498-007 / 598-005.