Details of CS5104 (Autumn 2025)
| Level: 5 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| CS5104 | Deep Learning | Dwaipayan Roy |
| Preamble |
|---|
| The timing of the chosen NPTEL course(s) should be concurrent to IISER-K class and exam schedule. The student has to register separately for the exam and write the proctored exam conducted by NPTEL in person at any of the designated exam centres. In case there is a delay or cancellation of the exam, the Department of CDS will conduct the exam.
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| Syllabus |
|---|
| Week 1 : (Partial) History of Deep Learning, Deep Learning Success Stories, McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm
Week 2 : Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Representation Power of Feedforward Neural Networks Week 3 : FeedForward Neural Networks, Backpropagation Week 4 : Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis Week 5 : Principal Component Analysis and its interpretations, Singular Value Decomposition Week 6 : Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders Week 7 : Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout Week 8 : Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization Week 9 : Learning Vectorial Representations Of Words Week 10: Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks Week 11: Recurrent Neural Networks, Backpropagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs Week 12: Encoder Decoder Models, Attention Mechanism, Attention over images |
| References |
|---|
| Books and references:
Deep Learning, An MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 1 | Not Allowed |
| 2 | IP | 3 | Not Allowed |
| 3 | MP | 1 | Not Allowed |
| 4 | MP | 3 | Not Allowed |
| 5 | MR | 1 | Not Allowed |
| 6 | MR | 3 | Not Allowed |
| 7 | MS | 3 | Not Allowed |
| 8 | MS | 5 | Not Allowed |
| 9 | MS | 7 | Not Allowed |
| 10 | MS | 9 | Elective |
| 11 | RS | 1 | Not Allowed |
| 12 | RS | 2 | Not Allowed |