This repository contains useful files for the advanced machine learning course. It will be updated as the course progresses to include tutorials (python and tensorflow), code samples from lecture, homework assignments, and term-project information.

In order to ensure that all students are working in a uniform environment, students will be expected to use Colab to follow along with in-class coding examples and solve homework assignments. We will also provide some term-project ideas of an appropriate computational such that the resources provided by Colab will be adequate. An introduction to Colab will be provided with the in-class python_tutorial.

Throughout the course, students will be expected to solve problems and create projects using the Python programming language. Prior knowledge or experience with the language is not assumed and a tutorial covering basic syntax, data structures, and libraries will be provided. Material pertaining to the the tutorial may be found in the python_tutorial folder.

For the second half of the semester, students will be expected to use Tensorflow's Python API in order to construct and train deep neural networks. Once again, prior knowledge or experience using Tensorflow (through the Python or any other API) is not assumed and a tutorial will be provided in lecture. Material pertaining to the the tutorial may be found in the tensorflow_tutorial folder.

PDFs containing the homework assignments may be found in the homeworks folder. Released solutions can be found on courseworks.

All questions pertaining to the course (organization, curriculum, material, ect.) should be asked on piazza where other students will be able to benefit from the answers. Questions containing sensitive &/or personal information can be directed to the TA’s who will redirect them to the professors as appropriate. No emails should be sent directly to the professors.

The TA’s will manage a Piazza forum for students to ask, and help answer, questions pertaining to course organization and material. Students are highly encouraged to browse previously answered questions and contribute answers to those still awaiting responses. Questions must be specific, self contained, and answerable with a single response. For example, a question troubleshooting the performance of a model needs to provide details about the model architecture, training procedure, dataset, and performance metrics. The TA’s will not be responsible for posting back and forth on the forums to extract these details. If your question does not fit well within these confines, it should be brought to office hours where that type of discussion is more appropriate. Furthermore, the piazza forums and office hours are specifically intended for discussion about the models and concepts covered in the course, NOT general Python or Tensorflow programming/debugging questions. Questions pertaining to these topics, in particular (but not limited to) “Why do I get this error message when I run my code?”, should be directed to appropriate online resources such as Stack Overflow.