# Syllabus (Spring 2022)

Acknowledgements

This class is being funded by a TensorFlow and Google AI award to support machine learning courses and diversity programs.

During the semester students will have access to NVIDIA's Deep Learning Institute materials.

## Description

Recent advances in Neural Networks and Deep Learning have made it possible to develop computational solutions to complex tasks usually performed by humans. Nowadays, well-trained Deep Learning architectures are able to achieve comparable or superhuman performance in complex tasks such as image classification/recognition, natural language understanding, speech recognition, machine translation, game playing, autonomous driving, and so forth.

In this course students will learn the fundamentals of deep learning architectures, including convolutional networks, recurrent neural networks, auto-encoders, and generative adversarial networks. Along the way, this course will also provide a basic introduction to basic machine learning topics, such as, supervised/unsupervised learning, gradient descent, generalization and overfitting, regularization, and model selection.

The course is open to advanced Undergraduate students and Graduate students. Students are expected to have good programming skills (preferably Python) and taken courses on machine learning, probability, statistics and linear algebra.

## Course Info

• Instructor: Prof. Marco Alvarez
• Lectures: TTh 2 - 3:15p @ Engineering 040
• Office Hours: Th 3:15-4:15p @ Tyler 255

## Support Tools and Technology

Students in this class will use the following platforms for communication, assignments, and grading. All students are required to use their primary email from eCampus for entering EdStem and Gradescope. We automatically register students.

All lecture materials and readings will be shared @ Ed Discussion

Assignments (programming, problem sets, paper reviews) 40%
Technical Presentation 20%
Final Project 40%

Your final letter grade will be calculated using the cutoffs in the table below. These cutoffs might be lowered, but they will not be raised. Your final letter grade will be the letter corresponding to the highest cutoff value less or equal than your final grade. Consider that those values are strict. For example, a final grade of 93.99 is an A- and not an A.

A    A-    B+    B    B-    C+    C    C-    D+    D    F
94   90    87    83   80    77    73   70    67    60   0