WEEK 9: RECAP WEEK

Recap week - the most important concepts of the first 8 weeks will be reviewed

November 10, 2025

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Weekly Materials

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Applications in NLP (at the end of the lecture; self-study)

Professor's lecture slides (PDF)
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TA Practice Slides

Hands-on tutorials and practice exercises

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Code Examples

Recap of Supervised Machine Learning: Regression Recap of Supervised Learning: Classification Recap of Stochastic Gradient Descent Feed-forward networks Sequence Modeling with Neural Networks (RNNs, LSTMs)
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References & Resources

Recap Materials Discussion of the previous exercises (with TA)

Additional Notes

Week 9: Recap Week

Overview

This week provides a comprehensive review of the most important concepts covered in the first 8 weeks of the course. It’s designed to consolidate learning and prepare students for the advanced topics in the remaining weeks.

Learning Objectives

  • Consolidate understanding of fundamental machine learning concepts
  • Review supervised learning approaches: regression and classification
  • Revisit optimization techniques and gradient descent methods
  • Strengthen knowledge of deep learning fundamentals
  • Practice with sequence modeling and neural networks

Topics Reviewed

  • Supervised Learning - Regression: Linear and polynomial regression recap
  • Supervised Learning - Classification: k-NN, Naive Bayes, Decision Trees
  • Optimization Methods: Gradient Descent and Stochastic Gradient Descent
  • Deep Learning Foundations: Feed-forward networks and backpropagation
  • Sequence Modeling: RNNs, LSTMs, and their applications
  • Applications in NLP: Brief introduction (self-study)

Schedule

  • Lecture: Monday, November 10, 2025 (10:15 - 12:00)
  • Practice Session: Monday, November 10, 2025 (16:30 - 18:00)
  • TA Session: Review and Q&A on previous exercises

Recap Materials

  • Comprehensive notebooks covering all major topics
  • Step-by-step implementation reviews
  • Problem-solving strategies and best practices
  • Common pitfalls and how to avoid them

Key Learning Reinforcements

  • Model Selection: Choosing appropriate algorithms for different problems
  • Evaluation Metrics: Understanding when to use different performance measures
  • Overfitting Prevention: Regularization and validation strategies
  • Practical Implementation: Framework usage and debugging techniques

Assignment

No new assignment this week - focus on reviewing and strengthening understanding of previous concepts. Use this time to catch up on any missed exercises or concepts.