Welcome

Course 2: Neural Networks & Decision Trees Overview

• Course Structure and Key Topics

• Neural Networks (Deep Learning)
  • Week 1: “Neural network inference and prediction”
  • Week 2: “Training neural networks with labeled data (X,Y)”
• Practical Machine Learning Systems
  • Week 3: Tips for efficient system building
    • “Avoid wasting time on approaches unlikely to work”
    • Guidance on systematic decision making
• Decision Trees
  • Week 4: Less hyped but “powerful and widely used”
  • High probability of practical application use

• Building ML Systems - Practical Decisions

• Critical choices:
  • Data collection vs. computational power
  • Resource allocation optimization
  • System architecture decisions
• Industry relevance
  • Even leading tech companies sometimes spend months on non-optimal approaches
  • Course provides framework to avoid such pitfalls

This course combines theoretical foundations of neural networks and decision trees with practical implementation guidance. Focus is on making effective decisions in real-world ML system development to avoid common pitfalls and inefficiencies.