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MLT: Week 4: Algorithms

Slides 
  • algorithms.pdf
  •  

 

Other material: 

(Optional) For an introduction/refresher on basics of neural networks, there are many web resources that are good, as well as most modern ML or deep learning books (e.g. https://www.deeplearningbook.org/). Andrew Glassner's Deep Learning: A Visual Approach, has many more intuitive diagrams. 

 

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Book traversal links for MLT: Week 4: Algorithms

  • MLT: Week 3: Bias, VC dimension and algorithms
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  • MLT: Week 5: Neural networks and Non-Convex optimisation

Navigation links

  • MLT: Resource List
  • MLT: Course Materials
    • MLT: Week 1: Introduction, formalization of ML and a simple ML problem
    • MLT: Week 2: Finite PAC learning
    • MLT: Week 3: Bias, VC dimension and algorithms
    • MLT: Week 4: Algorithms
    • MLT: Week 5: Neural networks and Non-Convex optimisation
    • MLT: Week 6: Privacy
    • MLT: Week 7: Privacy preserving Machine learning
    • MLT: Week 8: Fairness
    • MLT: Week 9: Interpretable ML
    • MLT: Week 10: Some Advanced concepts and exam
  • MLT: Tutorials
  • MLT: Assessment
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