| Sr. No. | Date | Lecture Contents | Reference |
|---|
| 1 | Aug 1, 2011 | Introduction to machine learning | Chapter 1 from Machine Learning by Tom Mitchell |
| 2 | Aug 2, 2011 | Introduction to machine learning | Chapter 1 from Machine Learning by Tom Mitchell |
| 3 | Aug 4, 2011 | Overview of target function representations | Chapter 1 from Machine Learning by Tom Mitchell |
| 4 | Aug 5, 2011 | Hypothesis class, version space | Chapter 1 and 2 from Machine Learning by Tom Mitchell |
| 5 | Aug 8, 2011 | Types of ML techniques, hypothesis selection through cross validation | Chapter 2 from Introduction to Machine Learning by Ethem Alppaydin |
| 6 | Aug 9, 2011 | Noise, bias-variance trade-off, under-fitting and over-fitting concepts | Chapter 2 from Introduction to Machine Learning by Ethem Alppaydin |
| 7 | Aug 11, 2011 | Q&A on over and under-fitting, bias-variance, Data: types of features, data normalization | Chapter 2 from Principles of Data Mining by David Hand et al. |
| 8 | Aug 12, 2011 | Bias variance trade-off using regression example | |
| 9 | Aug 16, 2011 | Correlation, covariance, Mahalanobis distance | Chapter 2 from Principles of Data Mining by David Hand et al. |
| 10 | Aug 18, 2011 | Mahalanobis distance, Minkowski distance, distance metric, Jaccard coefficient, missing values, feature transformations | Chapter 2, 3 from Principles of Data Mining by David Hand et al. |
| 11 | Aug 19, 2011 | Geometrical interpretation of Euclidean, Mahalanobis distance, dealing with uncertainty | Chapter 4 from Principles of Data Mining by David Hand et al. |
| 12 | Aug 22, 2011 | Maximum likeliHood estimation (MLE) theory and example using binomial distribution | Chapter 4 from Principles of Data Mining by David Hand et al. |
| 13 | Aug 23, 2011 | Maximum likeliHood estimation (MLE) of univariate Gaussian, generative vs discriminative models | Chapter 4 from Principles of Data Mining by David Hand et al. |
| 14 | Aug 25, 2011 | Maximum likelihood estimation of bivariate Gaussian distribution, sufficient statistics | Chapter 4 from Principles of Data Mining by David Hand et al. |
| 15 | Aug 26, 2011 | Bayesian Learning | Chapter 2 from Pattern Recognition and Machine Learning by Christopher M. Bishop |