| Date | Topic | Slides | Reading |
| Jan 20 (Tue) |
Introduction: data science life cycle, inferential thinking Case study: John Snow and the Broad Street Pump |
[CIT] Chapters 1, 2 [LDS] Chapter 1 |
|
| Jan 21 (Wed) |
Hypothesis Testing: Case study: Swain v. Alabama Case study: Alameda County Jury Pools |
PDF IPYNB, HTML |
[CIT] Chapters 11 |
| Jan 27 (Tue) |
Hypothesis Testing: Case study: Mendel's model Case study: Smoking and birth weight |
PDF IPYNB, HTML Data |
[CIT] Chapters 11, 12 |
| Jan 28 (Wed) |
Hypothesis Testing: Case study: RCT for lower back pain Case study: Deflategate Hypothesis Testing: Multiple testing |
PDF IPYNB, HTML Data: D1, D2, D3 |
[CIT] Chapters 11, 12 [ISL] Chapter 13 |
|
Feb 03 (Tue) Feb 04 (Wed) |
Hypothesis Testing: Multiple testing Parameter Estimation: The Bootstrap method, Confidence Intervals Case study: San Francisco compensation Quiz-1 |
PDF IPYNB, HTML Data: D1 |
[ISL] Chapter 13 [CIT] Chapter 13 |
| Feb 10 (Tue) |
Parameter Estimation: CI and p-value Mean, normal, and CLT: Markov, Chebychev, standard normal Case study: nba, flight delay, birth data |
PDF IPYNB, HTML Data: D1, D2, D3 |
[CIT] Chapter 14 |
| Feb 11 (Wed) |
Mean, normal, and CLT: standard normal, CLT Prediction: Pearson coefficient, Cauchy Schwarz Case study: flight delay, hybrid cars |
PDF IPYNB, HTML Data: D1, D2 |
[CIT] Chapter 14, 15 |
| Feb 17 (Tue) |
Linear Regression: Correlation, Regression line Case study: Family heights |
PDF IPYNB, HTML Data: D1 |
[CIT] Chapter 15 |
| Feb 18 (Wed) |
Linear Regression: Regression line, Best Fit Line Case study: Shotput |
PDF IPYNB, HTML Data: D1 |
[CIT] Chapter 15 |
| Feb 24 (Tue) |
Linear Regression: Evaluation, residuals Case study: Family Heights |
PDF IPYNB, HTML Data: D1 |
[CIT] Chapter 15 |
| Feb 25 (Wed) |
Linear Regression: Inference Classification: Nearest Neighbor Case study: Birth Data, Chronic Kidney Disease |
PDF IPYNB, HTML Data: D1, D2 |
[CIT] Chapter 16, 17 |
| Mar 24 (Tue) |
Classification: Nearest Neighbor Case study: Chronic Kidney Disease |
PDF IPYNB, HTML Data: D1 |
[CIT] Chapter 16, 17 |
| Mar 25 (Wed) |
Classification: Nearest Neighbor Multiple Linear Regression: Introduction Case study: Chronic Kidney Disease, Housing |
PDF IPYNB, HTML Data: D1, D2 |
[CIT] Chapter 17 |
| Mar 31 (Tue) |
Multiple Linear Regression: Analytical solution and Gradient Descent Case study: Housing |
PDF IPYNB, HTML Data: D1 |
[CIT] Chapter 17 |
| Apr 01 (Wed) |
Multiple Linear Regression: Stochastic/Batch GD, evaluation metrics Categorical variables, Polynomial regression Case study: Housing |
PDF IPYNB, HTML Data: D1 |
[LDS] Chapter 15, 16 |
| Apr 07 (Tue) | Multiple Linear Regression: Bias-Variance tradeoff, Ridge, Lasso | [LDS] Chapter 15, 16 | |
| Apr 08 (Wed) |
Multiple Linear Regression: Bias-Variance tradeoff Logistic regression: Log-likelyhood, Precision, Recall, Confusion matrix Generative Modeling: Introduction |
PDF IPYNB, HTML Data: D1, D2 |
[LDS] Chapter 15, 16, 19 |
| Apr 14 (Tue) |
Generative Modeling: Naive Bayes Unsupervised Learning: Introduction |
PDF IPYNB, HTML Data: D2 |
[LDS] Chapter 15, 16, 19 |
| Apr 15 (Wed) | Unsupervised Learning: SVD |
PDF |
[Foundations of Data Science] Chapter 3 |
| Apr 28 (Tue) | Unsupervised Learning: SVD |
PDF |
[Foundations of Data Science] Chapter 3 |
| Apr 29 (Wed) | Unsupervised Learning: SVD |
PDF |
[Foundations of Data Science] Chapter 3 |
| May 03 (Sun) | Unsupervised Learning: SVD, Clustering |
PDF |
[Foundations of Data Science] Chapter 3 |
| May 05 (Tue) | Unsupervised Learning: Clustering | Sanjoy Dasgupta's lecture notes [lec-1, lec-2] | |
| May 06 (Wed) | Unsupervised Learning: Clustering | Sanjoy Dasgupta's lecture notes [lec-1, lec-2] | |
| May 12 (Tue) |
Unsupervised Learning: Clustering Quiz-2 |
Sanjoy Dasgupta's lecture notes [lec-1, lec-2] | |
| May 13 (Wed) |
Unsupervised Learning: Clustering Dimension reduction: Johnson-Lindenstrauss |
PDF |
Sanjoy Dasgupta's lecture notes [lec-1, lec-2] |