BBM 406 - Fundamentals of Machine Learning
Fall 2023
Instructor: Ahmet Burak Can
Class Time: Tuesday, 13:00-15:45
Room: Computer Engineering Building, D10
Textbooks
- A Course in Machine Learning (CIML), Hal Daumé III, 2017 (available online)
- Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012 (available online)
- Pattern Recognition and Machine Learning, Bishop, Springer, 2006 (available online)
- Machine Learning: A Probabilistic Perspective, Murphy, MIT Press, 2012
- Probabilistic Machine Learning:An Introduction , Murphy, MIT Press, 2022
- Machine Learning, Tom Mitchell, McGraw Hill, 1997
Grading - BBM406
- Assignments - 15%
- Midterm exam - 35% (21/11/2023)
- Final exam - 50%
Grading - BBM409
- 3 Programming Assignments (done in pairs) 10%-25%-25%
- Programming Project (done in pairs) - 40%
Communication
- All class communication will be done via Piazza BBM406 communication group. Please register to this group on Piazza.com
- For the laboratory class of this course, please also register to Piazza BBM409 communication group
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Syllabus
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Introduction
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Machine Learning Methodology
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Concept Learning
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Reading:
Mitchell, Chapter 1
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KNN algorithm
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Reading:
CIML, Chapter 3.1-3.3
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Decision Trees
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Reading:
Mitchell, Chapter 3
CIML, Chapter 1
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Bayesian Learning
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Reading:
Mitchell, Chapter 6
Murphy 2012, Chapter 3.5
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Linear Regression, Cost Function, Gradient Descent
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Reading:
Barber, Chapter 14.1-14.2
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Logistic Regression
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Reading:
Bishop, Chapter 10
Murphy 2012, Chapter 8
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Support Vector Machines
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Reading:
Murphy, Chapter 14.5,14.2
Barber, Chapter 17.5
Bishop, Chapter 7.1
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Neural Networks
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Reading:
CIML, Chapter 10
Bishop, Chapter 7.1
Murhpy 2022, Chapter 13
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Introduction Deep Learning and Convolutional Neural Networks
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Reading:
Murhpy 2022, Chapter 13,14
CS231n at Standford Un.
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Unsupervised Learning, K-Means Clustering
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Reading:
CIML, Chapter 15
Bishop, Chapter 9.1
Murhpy 2022, Chapter 21
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Ensemble Learning: Bagging, Boosting
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Reading:
CIML, Chapter 13
Bishop, Chapter 14.1-3
Murhpy 2022, Chapter 18.2-5
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Acknowledgements
I thank to Prof. Ilyas Çiçekli and
Prof. Erkut Erdem at Hacettepe University, and
Prof. Eric Eaton at University of Pennsylvania for sharing their course slides publicly.
Presentations on this page are mostly adapted from their slides.
I also used some other public resources when constructing my course slides, I thank to all contributors.
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