Sample Syllabus

Course Description

This course, Introduction to Deep Learning with Python (DS311), introduces students to deep learning concepts, architectures, and libraries such as TensorFlow and PyTorch. Deep learning is a subset of machine learning that focuses on neural networks, which are modeled after the human brain’s structure and function. In this course, students will learn how to build and train neural networks for various applications.

Corequisites

• DA310: Introduction to Machine Learning with Python
• DA320: Mathematics for Machine Learning

Learning Objectives

By the end of this course, students will be able to:

• Understand the principles and concepts of deep learning.
• Implement neural networks using TensorFlow and PyTorch.
• Train and evaluate deep learning models.
• Apply deep learning techniques to real-world problems.

Course Structure

The course content will be presented through a series of lectures, hands-on workshops, and projects. Students will be evaluated through assignments, a final exam, and a data science project.

Assignments

Throughout the semester, students will be given assignments that will require them to implement and evaluate deep learning models using TensorFlow and PyTorch. These assignments will allow students to practice their deep learning skills.

Final Exam

At the end of the semester, students will take a final exam that will cover all the material presented in the lectures. The final exam will prioritize the understanding and application of deep learning concepts and techniques. The exam will count towards a significant portion of the overall course grade.

Schedule

The following is a general outline of the topics covered in the course:

Week	Topic
1	Introduction to Deep Learning
2	Neural Networks and Deep Learning Architectures
3	TensorFlow Basics
4	PyTorch Basics
5	Convolutional Neural Networks (CNNs)
6	Recurrent Neural Networks (RNNs)
7	Advanced Deep Learning Architectures
8	Model Optimization and Regularization
9	Transfer Learning and Fine-Tuning
10	Deep Learning for Natural Language Processing
11	Deep Reinforcement Learning
12	Final Exam Preparation and Course Reflection