Applied Machine Learning – Fall 2022

Course No: 0907726
Course Name: Applied Machine Learning

Microsoft Team: Link

Handouts:
Course Syllabus (pdf)
Slides:

  • Course Introduction (pdf)
  • Python
  • Python Introduction (pdf)
  • Python Basics (pdf)
  • Important Python Packages (pdf)
  • Advanced Python (pdf)
  • NumPy (pdf)
  • ML
  • Machine Learning Introduction (pdf) UPDATED
  • End-to-End Machine Learning Project (pdf) UPDATED
  • Information about the term project (pdf) UPDATED
  • Classification (pdf) UPDATED
  • Training Models and Regression (pdf) UPDATED
  • Classical Techniques (pdf) UPDATED
  • Unsupervised Learning and Clustering (pdf) UPDATED
  • Neural Networks (pdf) UPDATED
  • Artificial Neural Networks with Keras (pdf) UPDATED
  • Deep Neural Networks (pdf) UPDATED
  • Deep Computer Vision Using Convolutional Neural Networks (pdf) UPDATED
  • Recurrent Neural Networks (pdf) UPDATED
  • Reinforcement Learning (pdf) UPDATED
  • Recommender Systems (pdf) UPDATED

Videos:

  • Python
  • Python Introduction (YouTube)
  • Python Basics (YouTube)
  • Important Python Packages (YouTube)
  • Advance Python (YouTube)
  • Training Models (YouTube)
  • Classical Techniques 1: k-nearest neighbors (YouTube)
  • Classical Techniques 2: Support Vector Machines (YouTube)
  • Classical Techniques 3: Decision Trees (YouTube)
  • Classical Techniques 4: Ensemble Learning and Random Forests (YouTube)
  • Neural Networks (YouTube)
  • Artificial Neural Networks with Keras (YouTube: Part 1, Part 2, Part 3)
  • 13. Deep Neural Networks (YouTube: Part 1, Part 2, Part 3)
  • 14. Deep Computer Vision Using Convolutional Neural Networks (YouTube: Part 1, Part 2, Part 3)
  • 15. Recurrent Neural Networks (YouTube)
  • 16. Reinforcement Learning (YouTube: Part 1, Part 2)
  • 17. Recommender Systems (YouTube)

Homework:

Solutions:

  • Midterm exam (TBA), Final Exam (TBA)

Grades: Project and final exam marks (TBA)

Last update on 2/1/2023