| 1 |
6 Jan |
Introduction [Slides][Video] |
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| 2 |
8 Jan |
Convention, Metrics, Classification, Regression [Slides][Video] |
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| 3 |
11 Jan |
Decision Trees 1 [Slides][Video] |
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| 4 |
11 Jan |
Decision Trees 2, Bias/Variance [Slides][Video] |
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| 5 |
13 Jan |
Ensemble Methods [Slides][Video] |
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| 6 |
15 Jan |
Linear Regression 1, Maths for ML 1 [Slides1][Slides2][Video] |
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| 7 |
20 Jan |
Geometric Interpretation of least squares [Slides][Video] |
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| 7 |
20 Jan |
Linear Regression 2 [Slides][Video] |
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| 7 |
20 Jan |
Contour plot [Slides][Video] |
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| 8 |
22 Jan |
Gradient descent [Slides][Videos] |
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| 9 |
27 Jan |
Convexity [Slides][Video] |
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| 9 |
27 Jan |
Causality and Fairness (guest lecture by Ritik Dutta) [Slides][Video] |
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| 10 |
29 Jan |
Linear Regression (Time Complexity) [Slides][Video] |
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| 10 |
29 Jan |
Constrained Optimization [Slides][Video] |
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| 10 |
29 Jan |
Decision Trees (weighted samples) [Slides][Video] |
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| 11 |
3 Feb |
Constrained Optimization II [Video] |
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| 11 |
3 Feb |
Ridge Regression [Slides][Video] |
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| 12 |
5 Feb |
Ridge Regression II [Video] |
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| 12 |
5 Feb |
Lasso Regression [Slides][Video] |
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| 13 |
10 Feb |
Lasso Regression II [Video] |
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| 13 |
10 Feb |
Multivariate normal distribution [Slides][Video] |
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| 14 |
12 Feb |
Bayesian Learning 1 [Slides][Video] |
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| 14 |
12 Feb |
Bayesian Linear Regression [Slides][Video] |
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| 15 |
17 Feb |
Bayesian Linear Regression II [Video] |
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| 16 |
19 Feb |
Bayesian Linear Regression III [Video] |
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| 16 |
19 Feb |
Logistic Regression I[Slides][Video] |
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| 16 |
19 Feb |
Logistic Regression II [Video] |
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| 17 |
21 Mar |
HMM [Slides][Video1][Video2][Video3][Video4] |
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| 18 |
25 Mar |
Entropy, Differential Entropy [Slides1][Slides2][Video1][Video] |
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| 19 |
26 Mar |
Gaussian Processes I, II [Slides][Video1][Video2] |
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| 20 |
28 Mar |
Gaussian Processes: Learning parameters [Video] |
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| 21 |
8 Jun |
MLP 1 [Slides][Video] |
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| 22 |
10 Jun |
MLP 2 [Video1][Video2] |
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| 23 |
15 Jun |
NN: Backprop [Video] |
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| 24 |
17 Jun |
CNN [Slides][Video] |
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| 25 |
22 June |
SVM Introduction [Slides ][Video] |
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| 26 |
24 Jun |
SVM Kernels [Slides ][Video] |
Article from Jake Vanderplas’book |
| 27 |
29 Jun |
SVM soft margin [Slides][Video] |
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| 27 |
29 Jun |
Lagrangian and duality [Slides][Video] |
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| 28 |
1 Jul |
Automatic Differentiation [Slides][Video] |
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| 29 |
6 Jul |
Naive Bayes [Slides][Video] |
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| 30 |
8 Jul |
KNN [Slides][Video] |
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| 31 |
13 Jul |
Unsupervised learning [Slides][Video] |
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| 32 |
Feb |
Reinforcement Learning [Video] |
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