- Single variable and multi variable calculus
- A first course in ODEs (mandatory)
- A first course in PDEs (recommended)
- Complex functions
- A four-hour lecture course which provides four academic points.
- A weekly homework assignment will be provided.
- There will be a final exam.
- One of the questions at the final exam will be similar to one of the questions in the homework.
- The lecturer will prepare formula sheets for use at the final exam. These formula sheets will be available on the course website throughout the semester.
- A large collection of old exam will be available for students on the course website.
|Title||Analysis of single-cell RNAseq in order to learn about brain function|
|Study materials||Amit Zeisel 8.12.19|
Since 2012, deep neural networks are having an impressive practical success in many domains, yet their theoretical properties are not well understood. I will discuss why does neural network optimization, which based on local greedy steps, tend to converge to:
1) A global minimum, while many local minima exist.
2) A specific “good” global minimum in which the network function is surprisingly “simple” (while many “bad” global minima exist).
|Title||Why Neural Networks converge to “simple” solutions?|
|Study materials||About the deep learning era|
|Title||Projection-free Optimization and Learning|
|Study materials||Dan Graber 3.11|