CS5720 - Week 1
Slide 19 of 20

Course Roadmap & Expectations

📚 Course at a Glance

14
Weeks
280
Interactive Slides
3
Programming Projects
280+
Code Examples
Journey from beginner to practitioner: Learn neural networks and deep learning through hands-on experience, 3 major programming projects, and real-world applications. No heavy mathematics required - focus on understanding, building, and applying.

🗺️ 14-Week Learning Journey

1-2
Weeks 1-2
Foundation & Basic Training
Start your journey with neural network fundamentals, understand how they work, and train your first models.
Neural Network Basics Perceptrons Backpropagation Loss Functions
3-4
Weeks 3-4
Deep Learning & CNNs
Dive deeper into neural networks and learn about convolutional networks for image processing.
Deep Networks Convolutions Image Recognition CNN Architectures
📅 Week 4: Assignment 1 Due + Quiz 1
5-6
Weeks 5-6
Advanced CNNs & RNNs
Master advanced CNN techniques and learn recurrent networks for sequence data and time series.
Transfer Learning RNNs LSTM Sequence Modeling
7-8
Weeks 7-8
Advanced RNNs & Modern Concepts
Explore advanced RNN architectures and modern deep learning concepts like autoencoders and GANs.
Advanced RNNs Attention Autoencoders GANs
📝 Week 8: Midterm Exam
9-10
Weeks 9-10
Frameworks & Applications
Master deep learning frameworks and build complete computer vision and NLP applications.
TensorFlow/PyTorch Computer Vision Object Detection NLP Applications
📅 Week 9: Assignment 2 Due
📅 Week 10: Quiz 2
11-12
Weeks 11-12
NLP & Model Optimization
Deep dive into natural language processing and learn to optimize and deploy your models.
NLP with Deep Learning Transformers Model Optimization Deployment
13-14
Weeks 13-14
Ethics & Future Trends
Explore AI ethics, responsible development, and cutting-edge trends in deep learning.
AI Ethics Bias & Fairness Future Trends Career Development
📅 Week 13: Assignment 3 Due + Quiz 3
16
Week 16
Final Exam Week
Comprehensive examination covering all course material.
📝 Final Exam: 3-hour comprehensive exam (25% of grade)

🎯 What to Expect

🧠
Learning Approach
Hands-on, practical learning with minimal math. Focus on building intuition through interactive examples and real projects.
🛠️
Project-Based
Build 3 programming assignments: CNN image classifier, RNN/LSTM text analysis, and advanced deep learning application.
🤝
Support System
Course materials, detailed feedback on assignments, and comprehensive learning resources.
🚀
Skills You'll Gain
Build, train, and deploy neural networks. Understand when and how to apply different architectures to real problems.
🔧
Industry Tools
Master TensorFlow and PyTorch frameworks used in professional environments.
📈
Career Preparation
Portfolio development, technical interview prep, and guidance for ML engineering or research paths.

📊 Assessment Breakdown

30%
Programming Assignments
25%
Final Exam
20%
Midterm Exam
15%
Quizzes
10%
Participation

📅 Interactive Progress Tracker

0% Complete

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Prepared by Dr. Gorkem Kar