lectures

Lecture 1: Introduction (Sep. 02, 2020)

Lecture 2: Data + RegEx (Sep. 04, 2020)

Lecture 3: Web Scraping + PANDAS (Sep. 09, 2020)

Lecture 4: Advanced PANDAS (Sep. 11, 2020)

Lecture 5: kNN & Linear Regression (Sep. 13, 2020)

Lecture 6: Linear Regression (Sep. 16, 2020)

Lecture 7: Model Selection (Sep. 18, 2020)

Lecture 8: Probability (Sep. 21, 2020)

Lecture 9: Inference in Linear Regression (Sep. 23, 2020)

Lecture 10: Hypothesis Testing and Predictive CI (Sep. 25, 2020)

Lecture 11: Regularization (Sep. 28, 2020)

Lecture 12: Estimation of the Regularization Coefficients using CV and comparison (Sep. 30, 2020)

Lecture 13: Thinking critically about models, data, and debugging (Oct. 02, 2020)

Lecture 14: Visualization (Oct. 05, 2020)

Lecture 15: kNN classification and Logistic Regression I (Oct. 07, 2020)

Lecture 16: Case Study (Oct. 10, 2020)

Lecture 17: kNN classification and Logistic Regression II (Oct. 14, 2020)

Lecture 18: Multiclass Logistic Regression (Oct. 16, 2020)

Lecture 19: Missing Data (Oct. 19, 2020)

Lecture 22: Classification Trees (Oct. 21, 2020)

Lecture 20: PCA (Oct. 21, 2020)

Lecture 21: PCA & Missing Data (Oct. 23, 2020)

Lecture 23: Regression Trees, Bagging, and RF (Oct. 28, 2020)

Lecture 24: Tuning Hyperparameters (Oct. 30, 2020)

Lecture 25: Boosting Methods for Regression (Nov. 02, 2020)

Lecture 26: Boosting Methods for Classification (Nov. 04, 2020)

Lecture 27: Case Study 2 (Nov. 06, 2020)

Lecture 28: Neural Networks 1 - Perceptron & MLP (Nov. 09, 2020)

Lecture 29:Neural Networks 2 - Anatomy of NN & Design Choices (Nov. 11, 2020)

Lecture 30: Neural Networks 3: Design Choices II & Gradient Descent (Nov. 13, 2020)

Lecture 31: Neural Networks 4 -Back Propagation, SGD (Nov. 16, 2020)

Lecture 32: Regularization methods - Weight decay, data augmentation and dropout (Nov. 18, 2020)

Lecture 33: Full Example of Regression & Classification FFNN (Nov. 20, 2020)

Lecture 35: Interpreting Prediction Models (Nov. 30, 2020)

Lecture 36: Wrap-Up Review (Dec. 02, 2020)