| 1 | Course Overview | PDF unavailable |
| 2 | Course Overview (cont\'d) | PDF unavailable |
| 3 | Descriptive Statistics - Graphical Approaches | PDF unavailable |
| 4 | Descriptive Statistics - Measures of Central Tendency | PDF unavailable |
| 5 | Descriptive Statistics - Measures of Dispersion | PDF unavailable |
| 6 | Random Variables and Probability Distributions | PDF unavailable |
| 7 | Probability Distributions(cont\'d) | PDF unavailable |
| 8 | Probability Distributions(cont\'d) | PDF unavailable |
| 9 | Inferential Statistics - Motivation | PDF unavailable |
| 10 | Inferential Statistics - Single sample tests | PDF unavailable |
| 11 | Two Sample tests | PDF unavailable |
| 12 | Type 1 and Type 2 Errors | PDF unavailable |
| 13 | Confidence Intervals | PDF unavailable |
| 14 | ANOVA and Test of Independence | PDF unavailable |
| 15 | Short Introduction to Regression | PDF unavailable |
| 16 | Introduction to Machine Learning | PDF unavailable |
| 17 | Supervised Learning | PDF unavailable |
| 18 | Unsupervised Learning | PDF unavailable |
| 19 | Ordinary Least Squares Regression | PDF unavailable |
| 20 | Simple and Multiple Regression in Excel and Matlab | PDF unavailable |
| 21 | Regularization/ Coefficients Shrinkage | PDF unavailable |
| 22 | Data Modelling and Algorithmic Modelling Approaches | PDF unavailable |
| 23 | Logistic Regression | PDF unavailable |
| 24 | Training a Logistic Regression Classifier | PDF unavailable |
| 25 | Classification and Regression Trees | PDF unavailable |
| 26 | Classification and Regression Trees(cont\'d) | PDF unavailable |
| 27 | Bias Variance Dichotomy | PDF unavailable |
| 28 | Model Assessment and Selection | PDF unavailable |
| 29 | Support Vector Machines | PDF unavailable |
| 30 | Support Vector Machines(cont\'d) | PDF unavailable |
| 31 | Support Vector Machines for Non Linearly Separable Data | PDF unavailable |
| 32 | Support Vector Machines and Kernel Transformations | PDF unavailable |
| 33 | Ensemble Methods and Random Forests | PDF unavailable |
| 34 | Artificial Neural Networks | PDF unavailable |
| 35 | Artificial Neural Networks(cont\'d) | PDF unavailable |
| 36 | Deep Learning | PDF unavailable |
| 37 | Associative Rule Mining | PDF unavailable |
| 38 | Association Rule Mining (cont\'d) | PDF unavailable |
| 39 | Big Data, A small introduction | PDF unavailable |
| 40 | Big Data - A small introduction (cont\'d) | PDF unavailable |
| 41 | Clustering Analysis | PDF unavailable |
| 42 | Clustering Analysis (cont\'d) | PDF unavailable |
| 43 | Introduction to Experimentation and Active Learning | PDF unavailable |
| 44 | Introduction to Experimentation and Active Learning(cont\'d) | PDF unavailable |
| 45 | An Introduction to Online Learning - Reinforcement Learning | PDF unavailable |
| 46 | An Introduction to Online Learning - Reinforcement Learning (cont\'d) | PDF unavailable |
| 47 | Summary+ Insights into the Final Exam | PDF unavailable |
| 48 | Tutorial on Weka | PDF unavailable |
| 49 | Tutorial on Decision Trees | PDF unavailable |