| Lecture | Topic(s) | Reference* | Dates | Homework / Remarks |
| 1 | Introduction, origins, importance, descriptive statistics | FPP 3 | 02-Jan | Plotting Histograms |
| 2 | Measures of centrality, dispersion, moments | FPP 4 | 04-Jan | PS 1 |
| 3 | Probability basics, counting | FPP 13, W 1.1 | 09-Jan | Coin toss simulation |
| 4 | Conditional Probability, Bayes theorem, Independence, Random variable | FPP 13-14, W 1.5-1.9 | 11-Jan | Testing Independence, PS 2 |
| 5 | Probability distributions, Multivariate distributions, Marginal distribution | W 2.1-2.6 | 16-Jan | Gaussian table |
| 6 | Expectation, Moment generating functions, Jensen's inequality, Random walk | W3.1-3.6 | 18-Jan | Random walk: microbit demo, PS 3 |
| 7 | Probability inequalities, WLLN, Convergence of random variables | W4, 5.1-5.3 | 23-Jan | |
| 8 | Central limit theorem | W 5.4 | 25-Jan | PS 4 |
| 9 | Sampling from a distribution, Monte-Carlo integration, MCMC | W 24.2-24.5 | 30-Jan | Monte-Carlo demo, PS4 |
| 10 | Normal approximation for data, measurement errors | FPP 5-7 | 01-Feb | Quiz |
| 11 | Correlation: Bivariate distribution, scattered plot, correlation coefficient, features, correlation vs causation | FPP 8-9 | 06-Feb | |
| 12 | Curve fitting, least square method, regression | FPP 10-12 | 08-Feb | |
| 13 | Correlation, regression and curve fitting problems | FPP 8-12 | 13-Feb | PS 5, Using software for curve fitting, Case study |
| 14 | Design of experiments | FPP 1-2 | 15-Feb | |
| MID SEM EXAMS | 17-Feb to 23-Feb | |||
| 15 | Sampling theory 1: basics | FPP 16-18, 21 | 27-Feb | |
| 16 | Sampling theory 2: methods | FPP 19,20,22,23 | 29-Feb | PS 6 |
| 17 | Sampling theory 3: test of significance (hypothesis testing), z-test | FPP 26-27, W 10.1-2 | 01-Mar | |
| 18 | Sampling theory 4: t-test, chi-square test, test for randomness | FPP 26-28, W 10.3, 10.10.2 | 05-Mar | Hypothesis testing on R |
| 19 | Sampling theory 5: test for difference between two means and variances, Fisher's z-distribution | FPP 27 | 07-Mar | PS 7 |
| 20 | Statistical inference 1: Theory of estimation, Cramer-Rao bound, Fisher inequality, max likelihood | 12-Mar | ||
| 21 | Statistical inference 2: Neyman-Pearson lemma, power of test, types of error | W 10.10.1 | 14-Mar | |
| 22 | Advanced Topics: EM algorithms, f-divergence | 21-Mar | ||
| 23 | Statistical learning introduction, bias-variance tradeoff, regression, parameter estimation | W 20.1, JWHT 1-2 | 28-Mar | |
| 24 | Multiple linear regression, qualitative prediction | JWHT 3 | 02-Apr | |
| 25 | Perceptron, logistic regression, multiple linear regression, LDA | JWHT 4.1-3 | 04-Apr | |
| 26 | Project Presentation | 09-Apr | ||
| 27 | Course Summary | 11-Apr |