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| 1 |
4 Jan |
Introduction, Logistics |
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| 2 |
8 Jan |
Accuracy metrics, classification, regression |
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Decision Trees (Discrete Input, Discrete Output) |
HW1 |
Tom Mitchell Chap 3 |
| 3 |
9 Jan |
Decision Trees 2 |
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Decision Trees 3 |
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Bias-Variance and Cross-Validation |
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| 4 |
11 Jan |
Ensemble learning - bagging, boosting and Random Forest |
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11 Jan |
Research Methods (See RM101 here) |
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15 Jan |
Quiz 1 (solved) |
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| 5 |
15 Jan |
Linear Regression 1 |
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An Introduction to Statistical Learning with Applications in R Chapter 3 |
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15 Jan |
Maths for ML 1 |
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17 Jan |
HW 2 due on 27th Jan |
HW2 |
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| 6 |
18 Jan |
Paper presentations I |
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20 Jan |
Project proposal due |
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| 7 |
22 Jan |
Linear Regression 2 (slides in 15th Jan lecture) |
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| 8 |
25 Jan |
Maths for ML 2 (Contour, Gradients, Lagrangian) |
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Contrained Optimisation from Khan Academy |
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Feature Selection |
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Linear Regression 3 - Gradient Descent |
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27 Jan |
HW 3 due on 5th Feb |
HW3 |
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| 9 |
29 Jan |
Paper presentations II |
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| 10 |
1 Feb |
SGD and GD worked out example |
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1 Feb |
Notebook motivating regularisation |
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An overview of GD algorithm |
| 11 |
5 Feb |
Paper presentations III |
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| 12 |
8 Feb |
SGD v.s GD Summary |
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8 Feb |
Notebook showing Contour for GD and SGD |
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8 Feb |
Shuffling |
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8 Feb |
KKT conditions |
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KKT conditions |
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8 Feb |
Convexity |
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8 Feb |
Ridge Regression |
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| 13 |
12 Feb |
Subgradient |
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12 Feb |
Lasso |
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12 Feb |
Coordinate Descent |
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12 Feb |
Coordinate Descent for Regularised and Unregularised Regression |
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| 14 |
15 Feb |
Phase - I presentations |
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| 15 |
18 Feb |
KNN |
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18 Feb |
Parametric v/s Non-Parametric methods |
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2.1.2 in ISLR book |
| 16 |
12 Mar |
Why KNN is non-parametric |
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12 Mar |
Curse of dimensionality |
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12 Mar |
Naive Bayes |
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https://www.coursera.org/learn/bayesian-methods-in-machine-learning |
| 17 |
19 Mar |
ML categorisation |
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19 Mar |
Active Learning notebook |
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19 Mar |
Active learning |
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Survey on Active learning |
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20 Mar |
Phase - II presentations |
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| 18 |
22 Mar |
MLE, MAP, Bayesian -1 |
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| 19 |
26 Mar |
MLE, MAP, Bayesian -2 |
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Multivariate Gaussian |
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26 Mar |
Bayesian Linear regression notebook |
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26 Mar |
SVM 1 |
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SVM without tears |
| 20 |
29 Mar |
SVM 2 |
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SVM tutorial |
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ML demo presentations |
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| 21 |
2 Apr |
SVM 3 |
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| 22 |
5 Apr |
Demos |
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| 23 |
9 Apr |
SVM 4 |
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| 23 |
9 Apr |
Logistic Regression |
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| 24 |
12 Apr |
SVM dual primal relationship |
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| 24 |
12 Apr |
Neural Networks 1 |
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| 25 |
16 Apr |
Quiz 3 (solved) |
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| 25 |
16 Apr |
Neural Networks - II (slides same as above) |
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| 26 |
23 Apr |
Dropout notebook |
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| 26 |
23 Apr |
Unsupervised Learning |
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2 May |
End semester exam |
Answers |
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