Artificial Intelligence

Master’s Program in Deep Learning

The curriculum ensures a comprehensive understanding of both the theoretical and practical aspects of deep learning, preparing graduates for cutting-edge ... Show more

Instructor

Aayushi Mathpal

Description
Curriculum

Module 1: Introduction to Deep Learning

1

Lecture 1: History and development of neural networks.

1 hour
2

Lecture 2: Key concepts in deep learning.

1 hour
3

Lecture 3: Comparison with traditional machine learning.

1 hour
4

Lecture 4: Building basic neural networks with TensorFlow and Keras.

1 hour
5
Resources:

Text lesson
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

TensorFlow Documentation.

Module 2: Neural Networks and Back propagation

6

Lecture 1: Perceptrons and activation functions.

1 hour
7

Lecture 2: Feed forward neural networks.

1 hour
8

Lecture 3: Backpropagation algorithm.

1 hour
9

Lecture 4: Implementing neural networks from scratch using NumPy.

1 hour
10
Resources:

1 hour
"Neural Networks and Deep Learning" by Michael Nielsen.

NumPy Documentation.

Module 3: Optimization Techniques

11

Lecture 1: Gradient descent.

1 hour
12

Lecture 2: Stochastic gradient descent.

1 hour
13

Lecture 3: Adam, RMSprop, and other optimization algorithms.

1 hour
14

Lecture 4: Utilizing TensorFlow and Keras for optimization.

1 hour
15
Resources:

1 hour
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.

Keras Documentation.

Module 4: Convolutional Neural Networks (CNNs)

16

Lecture 1: Convolutional layers.

1 hour
17

Lecture 2: Pooling layers.

1 hour
18

Lecture 3: Architectures like LeNet, AlexNet.

1 hour
19

Lecture 4: Building and training CNNs for image classification tasks.

1 hour
20
Resources:

1 hour
"Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani.

PyTorch Tutorials.

Module 4: Convolutional Neural Networks (CNNs) Continued

21

Lecture 5: VGG, ResNet.

1 hour
22

Lecture 6: Transfer learning in CNNs.

1 hour
23

Lecture 7: Practical applications of CNNs.

1 hour
24

Lecture 8: Implementing transfer learning with pre-trained models.

1 hour
25
Resources:

1 hour
"Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani.

PyTorch Tutorials.

Module 5: Recurrent Neural Networks (RNNs)

26

Lecture 1: RNN architectures.

1 hour
27

Lecture 2: Long Short-Term Memory (LSTM).

1 hour
28

Lecture 3: Gated Recurrent Unit (GRU).

1 hour
29

Lecture 4: Implementing RNNs for time series forecasting.

1 hour
30
Resources:

1 hour
"Deep Learning with Python" by François Chollet.

Keras RNN Guide.

Module 5: Recurrent Neural Networks (RNNs) Continued

31

Lecture 5: Sequence prediction.

1 hour
32

Lecture 6: Natural language processing applications.

1 hour
33

Lecture 7: Building RNNs for text generation.

1 hour
34

Lecture 8: Advanced techniques in NLP with RNNs.

1 hour
35
Resources:

1 hour
"Deep Learning with Python" by François Chollet.

Keras RNN Guide.

Module 6: Generative Models

36

Lecture 1: Introduction to Generative Adversarial Networks (GANs).

1 hour
37

Lecture 2: Variational Autoencoders (VAEs).

1 hour
38

Lecture 3: Applications in image generation.

1 hour
39

Lecture 4: Creating GANs to generate synthetic data.

1 hour
40
Resources:

1 hour
"GANs in Action" by Jakub Langr and Vladimir Bok.

PyTorch Generative Adversarial Network (GAN) Tutorial.

Module 7: Reinforcement Learning

41

Lecture 1: Introduction to reinforcement learning.

1 hour
42

Lecture 2: Markov Decision Processes.

1 hour
43

Lecture 3: Q-learning.

1 hour
44

Lecture 4: Deep Q-Networks (DQNs).

1 hour
45
Resources:

1 hour
"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto.

OpenAI Gym Documentation.

Module 7: Reinforcement Learning Continued

46

Lecture 5: Policy gradients.

1 hour
47

Lecture 6: Actor-critic methods.

1 hour
48

Lecture 7: Applications of reinforcement learning.

1 hour
49

Lecture 8: Developing RL algorithms using TensorFlow.

1 hour
50
Resources:

1 hour
"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto.

OpenAI Gym Documentation.

Module 8: AI Ethics and Privacy

51

Lecture 1: Ethical considerations in AI.

1 hour
52

Lecture 2: Bias and fairness in AI models.

1 hour
53

Lecture 3: Privacy and security issues.

1 hour
54

Lecture 4: Tools for ensuring ethical compliance in AI models.

1 hour
55
Resources:

1 hour
"Ethics of Artificial Intelligence and Robotics" by Vincent C. Müller.

AI Fairness 360 Documentation.

Module 9: Capstone Project and Business Applications

56

Lecture 1: Advanced techniques in business intelligence.

1 hour
57

Lecture 2: Data visualization and strategic decision-making.

1 hour
58

Lecture 3: Creating interactive dashboards with Matplotlib and Seaborn.

1 hour
59

Lecture 4: Capstone project integration.

1 hour
60
Resources:

1 hour
"Business Intelligence Guidebook" by Rick Sherman.

Matplotlib and Seaborn Documentation.

Master’s Program in Deep Learning

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