Machine Learning and Computation Statistics
This is a teaching partnership between the Africa Center of Excellence in Data Science at the University of Rwanda and the Institute of Applied Computational Science at Harvard University.
Week 2: Models for Classification
Day 1: Models for Classification
Topic 1: Models for Classification
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Lecture Video:
(8:02 minutes) - Lecture Slides: Slides
- References:
- “Introduction to Statistical Learning”: Chapter 4.1, 4.2, 4.3
- Understanding Logistic Regression in Python
- Concept Quiz: Quiz
Topic 2: Gradient Descent
Topic 3: Evaluating and Interpreting Classifiers
In-Class Exercise: Notebook on Classification
Day 2: Evaluating Classifiers
Topic 1: Non-Probabilistic Models for Classification
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Lecture Video:
(9:45 minutes) - Lecture Slides: Slides
- References:
- “Introduction to Statistical Learning”: Chapter 8.1.2, 8.1.3, 8.1.4
- Decision Trees in Machine Learning
- Concept Quiz: Quiz
Topic 2: Hyper-parameter Selection and Variance Reduction
- Lecture Video:
(9:42 minutes) - Lecture Slides: Slides
- References:
- “Introduction to Statistical Learning”: Chapter 8.2.1, 8,2,2, 5.1, 5.2
- Understanding Random Forest
- Concept Quiz: Quiz
Topic 3: Evaluating Classifiers
In-class Exercise: Non Probabilistic Classifiers and Model Evaluation
Day 3: Introduction to Neural Networks
Topic 1: Neural Networks for Regression
Topic 2: Optimizing Neural Networks
Topic 3: Implementing Neural Networks in keras
In-class Exercise: Neural Networks for Regression
Day 4: More on Neural Networks
Topic 1: Neural Networks for Classification
Topic 2: Interpreting Neural Networks
Topic 3: Regularization for Neural Networks
In-Class Exercise: Neural Networks for Classification