This document outlines the details of a deep learning course including course objectives, learning outcomes, modules, textbook and reference materials, lecture plan, and evaluation scheme. The course aims to teach state-of-the-art deep learning algorithms and their applications through designing neural network architectures. Key topics include feedforward networks, convolutional neural networks, recurrent networks, autoencoders and sequence modeling. Student performance is evaluated through internal assessments, midterm and comprehensive exams.

1. Course Code Course Title L T P U
CSE431 DEEP LEARNING 3 0 0 3
Course Objectives: The student learns various state-of-the-art deep learning algorithms and their applications
to solve real-world problems. The student develops skills to design neural network architectures and training
procedures using various deep learning platforms and software libraries.
Course Learning Outcomes:
On completing this course, the student will be able to:
CO1: describe the feedforward and deep networks.
CO2:design single and multi-layer feed-forward deep networks and tune various hyper-parameters.
CO3: analyze the performance of deep networks.
Module-1
Introduction to machine learning-
Linear models (SVMs and Perceptron’s, logistic regression)- Intro to Neural Nets: What a shallow network
computes- Training a network: loss functions, back propagation and stochastic gradient descent- Neural
networks as universal function approximates
Unit-II
Historical context and motivation for deep learning; basic supervised classification task, optimizing logistic
classifier using gradient descent, stochastic gradient descent, momentum, and adaptive sub gradient method.
Feed forward neural networks, deep networks, regularizing a deepnetwork, model exploration, and hyper
parameter tuning.
Unit-III
Convolution Neural Networks: Introduction to convolution neural networks: stacking, striding and pooling,
applications like image, and text classification.
Unit-IV
Sequence Modeling: Recurrent Nets: Unfolding computational graphs, recurrent neural networks (RNNs),
bidirectional RNNs, encoder-decoder sequence to sequence architectures, deep recurrent networks.GAN
Unit-V
Auto encoders: Under complete auto encoders, regularized auto encoders, sparse auto encoders, denoising auto
encoders, representational power, layer, size, and depth of auto encoders, stochastic encoders and decoders.
LSTM
Text book(s):
T1. Ian Goodfellow, Deep Learning, MIT Press, 2016.
T2. Jeff Heaton, Deep Learning and Neural Networks, Heaton Research Inc, 2015.
Reference Book:
R1. Mindy L Hall, Deep Learning, VDM Verlag,2011.
R2. Li Deng (Author), Dong Yu, Deep Learning: Methods and Applications (Foundations and Trends in Signal
Processing), Now Publishers Inc, 2009.

2. Lecture-wise plan:
Lecture
No.
Learning objective Topics to be covered
Reference (Sec.
No. of Text /Ref
Books)
1 - 2
Introduction to machine
learning
Different ML methods
T1: Page:1-8
3-10
Intro to Neural Nets: What a
shallow network computes-
Training a network: loss
functions, backpropagation
and stochastic gradient
descent- Neural networks as
universal function
approximates
Intro to Neural Nets: What a
shallow network computes-
Training a network: loss
functions, backpropagation
and stochastic gradient
descent- Neural networks as
universal function
approximates
R1: Page:30-42
T2: Page:20-35
11- 15
Historical context and
motivation for deep learning;
basic supervised
classification task,
optimizing logistic classifier
using gradient descent
Historical context and
motivation for deep learning;
basic supervised
classification task,
optimizing logistic classifier
using gradient descent
T1: Page:22-34
15- 19
momentum, and adaptive
sub gradient method.
momentum, and adaptive
sub gradient method. T2: Page:51-62
20 - 29
Convolution Neural
Networks: Introduction to
convolution neural networks
stacking, striding and
pooling, applications like
image, and text
classification.
R2: Page:153-167
R1: pages:46-65
30 - 35
Sequence Modeling:
Recurrent Nets: Unfolding
computational graphs,
recurrent neural networks
(RNNs),
Sequence Modeling:
Recurrent Nets: Unfolding
computational graphs,
recurrent neural networks
(RNNs),
T1: Page:37-49

3. 36-39
bidirectional RNNs,
encoder-decoder sequence to
sequence architectures, deep
recurrent networks
bidirectional RNNs,
encoder-decoder sequence to
sequence architectures, deep
recurrent networks
T1: Page: 70-89
40 -45
Autoencoders:
Undercompleteautoencoders,
regularized autoencoders,
sparse autoencoders,
denoisingautoencoders,
representational power,
layer, size, and depth of
autoencoders, stochastic
encoders and decoders
Autoencoders:
Undercompleteautoencoders,
regularized autoencoders,
sparse autoencoders,
denoisingautoencoders,
representational power,
layer, size, and depth of
autoencoders, stochastic
encoders and decoders
T1: Page :95-120
Evaluation Scheme:
Component Duration
Weightage
(%)
Remarks
Internal I 25
Mid Term Exam 2 hours 20 Closed Book
Internal II 25
Comprehensive
Exam
3 hours 30 Closed Book
1. Attendance Policy: A Student must normally maintain a minimum of 75% attendance in the course
without which he/she shall be disqualified from appearing in the respective examination.
2. Make-up Policy: A student, who misses any component of evaluation for genuine reasons, must
immediately approach the instructor with a request for make-up examination stating reasons. The decision of
the instructor in all matters of make-up shall be final.
3. Chamber Consultation Hours: During the Chamber Consultation Hours, the students can consult the
respective faculty in his/her chamber without prior appointment.