List of topics (in order) and readings, along with links to videos and slides used in videos.
This page is currently being edited. Some links are not connected yet.
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| 01 |
Chapter 1: Introduction to Machine Learning |
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| 02 |
Chapter 2: Learning Base R |
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| 03 |
Chapter 2: R -- cont'd |
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| 04 |
Chapter 3: Data Visualization in R & Chapter 4: ML Workflow |
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| 05 |
Chapter 5: Linear Regression |
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| 06 |
Chapter 5: Linear Regression -- cont'd |
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| 07 |
Chapter 6: Logistic Regression |
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| 08 |
Chapter 6: Logistic Regression -- cont'd |
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| 09 |
Chapter 7: Naive Bayes |
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| 10 |
Chapter 7: Naive Bayes -- cont'd |
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| 11 |
Chapter 8: Inductive Learning; Feature Selection |
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| 12 |
Chapters 9 and 10: Modern R |
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Exam 1 |
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| 13 |
Chapter 12: kNN |
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| 14 |
Chapter 13: Clustering |
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| 15 |
Chapter 14: Decision Trees |
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| 16 |
Chapter 15: Feature Engineering; PCA and LDA |
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| 17 |
Chapter 16: Support Vector Machines |
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| 18 |
Chapter 17: Ensemble Methods |
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| 19 |
Chapter 18: PAC Learning |
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Exam 2 |
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| 20 |
Chapter 19: Python Basics |
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| 21 |
Chapter 20: Python ML Libraries |
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| 22 |
Chapter 21: Python ML Examples |
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| 23 |
Chapter 22: Data Wrangling in Python |
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| 24 |
Chapter 23: Neural Networks |
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| 25 |
Chapter 24: Deep Learning |
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| 26 |
Chapter 25: Domingo's 5 Tribes of ML Algorithms |
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| 27 |
Chapter 26: Bayes Nets |
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| 28 |
Chapter 27: Markov Models |
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| 29 |
Chapter 28: Reinforcement Learning |
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Exam 3 |
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| Textbook: |
Machine Learning Handbookby Karen MazidiMy students don't need to purchase the book because I will provide the pdf. |
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