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)
- End-to-End Machine Learning Project (pdf)
- Information about the term project (pdf)
- Classification (pdf)
- Training Models and Regression (pdf)
- Classical Techniques (pdf)
- Unsupervised Learning and Clustering (pdf)
- Neural Networks (pdf)
- Artificial Neural Networks with Keras (pdf)
- Deep Neural Networks (pdf)
- Deep Computer Vision Using Convolutional Neural Networks (pdf)
- Recurrent Neural Networks (pdf)
- Reinforcement Learning (pdf)
- Recommender Systems (pdf)
Videos:
- Lecture 1: Course Introduction and ML Introduction (Part 1, Part 2)
- Lecture 2: ML Introduction (Slide 28 to 46) and Python (Link)
- Lecture 3: Python Basics, Important Python Packages (Link)
- Lecture 4: Important Python Packages, Advance Python (Part 1, Part 2)
- Lecture 5: Chapter 2: End-to-End Machine Learning Project (Link)
- Lecture 6: Chapter 2: End-to-End Machine Learning Project (Slides 26 to 58, Link), (Slides 59 to end, Link)
- Lecture 7: Chapter 3 Classification (Slides 1 to 29, Link), (Slides 30 to end, Link)
- Lecture 8: Chapter 4: Training Models and Regression (Link)
- Lecture 9: Chapters 5, 6, & 7: Classical Techniques (Slides 1 to 25, Link), (Slides 26 to end, Link)
- Lecture 10: Chapter 8 & 9: Unsupervised Learning and Clustering (Link)
- Lecture 11: Chapter 10: Neural Networks (Link, until Minute 1:13), Artificial Neural Networks with Keras (Slides 1 to 23, Link), (Slides 24 to end, Link)
- Lecture 12: Chapter 11: Training Deep Neural Networks (Link)
- Lecture 13: Recurrent Neural Networks and Reinforcement Learning (Link)
- Lecture 14: Recommender Systems (YouTube)
Homework:
Solutions:
- Midterm exam (TBA), Final Exam (TBA)
Grades: Project and final exam marks (TBA)
Last update on 4/6/2023