Course content

In this course, in addition to getting a solid understanding of deep learning, you will get hands-on experience by solving practical, real-life tasks using state-of-the-art techniques and software frameworks from machine learning, and deep learning. The concepts covered in this course provide relevant theoretical and hands-on programming knowledge. 

This course covers the following topics:

• Introduction to Deep Learning (Deep Learning Fundamentals, AI vs. Machine Learning vs. Deep Learning - Relationship Overview)

• Artificial Neural Networks (Perceptrons, Intro to Artificial Neural Networks, Layers in Artificial Neural Networks, Activation Functions in Artificial Neural Networks, Loss Functions in Artificial Neural Networks, Training Artificial Neural Networks, Batch Size & Epochs in Artificial Neural Networks, Optimization Algorithms in Artificial Neural Networks, Learning Rates in Artificial Neural Networks, Backpropagation Intuition - Neural Network Training, Bias in Artificial Neural Networks) .

• Additional Fundamental topics (Datasets for Deep Learning - Training, Validation, & Test Sets, Overfitting- Artificial Neural Networks, Underfitting- Artificial Neural Networks, Tensor flow, and Keras, Multi-Layer Perceptron (MLP), Classification with the Tensor flow and MLP, Regression with the Tensor flow and MLP)

• Convolutional Neural Networks (CNNs) (What are CNNs? Visualizing convolutional filters, Zero padding, Max pooling)

• Recurrent Neural Networks (RNN) (What are RNNs? Architecture, Applications of RNNs)

• LSTM (General structure of LSTM neural network, RNN neural network, and long-term dependence, LSTM neural network and long-term dependence, LSTM network architecture)

Learning outcome

After taking this course, you will:

• have a firm understanding of the most fundamentals of modern neural networks and their practical use

• Identify the deep learning algorithms which are more appropriate for various types of learning tasks in various domains.

• Implement deep learning algorithms and solve real-world problems.

Teaching

• Teaching language: English

• Weekly lectures published as videos

• One hands-on use-case based assignments will be assigned to the students

• Three voluntary live sessions will be conducted to discuss the solutions for each assignment

• Course Starts: Monday, September 04 with an online kick-off meeting

• Assignment #1
  • Start: September 25
    • Live discussion on October 2
  • End: October 13

• Assignment #2
  • Start: October 16
    • Live discussion on October 23
  • End: November 3

• Course End: November 10

Examination

• The assignment is mandatory - approved / not approved
• Submissions should provide coding solutions to the respective problems with proper documentation
• Individual home exam. [Grade: A-F]
• Exam date: 13.11.2024 (duration: one week)

Admission requirements

• Foreign applicants must document education and English skills in accordance with NOKUT's regulations.

Admission

• We practice the first-come, first-served principle when registering this course, as long as you qualify for admission based on the admission requirements and the prerequisite knowledge

Required prerequisite knowledge

• None

Recommended prerequisite knowledge

• E-DAT120 Introduction to Programming

Syllabus

• The syllabus are the lecture slides with references.

The course will be set up as long as there is a sufficient amount of applicants

In this course, in addition to getting a solid understanding of deep learning, you will get hands-on experience by solving practical, real-life tasks using state-of-the-art techniques and software frameworks from machine learning, and deep learning. The concepts covered in this course provide relevant theoretical and hands-on programming knowledge.  This course covers the following topics: • Introduction to Deep Learning (Deep Learning Fundamentals, AI vs. Machine Learning vs. Deep Learning - Relationship Overview) • Artificial Neural Networks (Perceptrons, Intro to Artificial Neural Networks, Layers in Artificial Neural Networks, Activation Functions in Artificial Neural Networks, Loss Functions in Artificial Neural Networks, Training Artificial Neural Networks, Batch Size & Epochs in Artificial Neural Networks, Optimization Algorithms in Artificial Neural Networks, Learning Rates in Artificial Neural Networks, Backpropagation Intuition - Neural Network Training, Bias in Artificial Neural Networks) .

• Additional Fundamental topics (Datasets for Deep Learning - Training, Validation, & Test Sets, Overfitting- Artificial Neural Networks, Underfitting- Artificial Neural Networks, Tensor flow, and Keras, Multi-Layer Perceptron (MLP), Classification with the Tensor flow and MLP, Regression with the Tensor flow and MLP)

• Convolutional Neural Networks (CNNs) (What are CNNs? Visualizing convolutional filters, Zero padding, Max pooling)

• Recurrent Neural Networks (RNN) (What are RNNs? Architecture, Applications of RNNs)

• LSTM (General structure of LSTM neural network, RNN neural network, and long-term dependence, LSTM neural network and long-term dependence, LSTM network architecture)