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Lecture 1: Introduction to Data Science
Lecture 2: Review of Linear Algebra
Lecture 3: Review of Probability Theory
Lecture 4: Linear Regression
Lecture 6: Logistic Regression
Lecture 7: Gradient Descent
Lecture 8: K-means
Lecture 9: Information Theory
Lecture 10: Midterm Review
Lecture 11: Midterm Solutions
Lecture 12: Entropy
Lecture 13: Decision Trees
Lecture 14: Random Trees Example
Lecture 15: Expectation
Lecture 16: Estimation
Lecture 17: Covariance
Lecture 18: Covariance Matrix

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Mini-Project 1: Simulations & Visualization
Mini-Project 2: Logistic Regression & Disaster Survival
titanic_data.csv
Mini-Project 3: K-means & Breast Cancer
breast_data.csv
breast_truth.csv
mu_init.mat
Mini-Project 4: Data Science & Information Theory
Mini-Project 5: Correlation & Brain fMRI
thumb_data.mat
Mini-Project 6: PCA & Face Clustering
Yale_data.mat