Advanced Machine Learning

Course name: Advanced Machine Learning
Course number: DS-02
Area of instruction: Computer Science and Engineering

Administrative details

Faculty: Computer Science and Engineering
Year of instruction: 2nd year of MSc
Semester of instruction: 2nd semester
No. of Credits: 5 ECTS
Total workload on average: 180 hours overall
Frontal lecture hours: 2 hours per week.
Frontal tutorial hours: 0 hours per week.
Lab hours: 2 hours per week.
Individual lab hours: 2 hours per week.
Frequency: weekly throughout the semester.
Grading mode: letters: A, B, C, D.

Course outline

This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover sampling methods, Splines, Kernel Hilbert spaces, Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularizations, Bayesian Learning and networks, Graphical Models (HMM), Autoencoders, Generative models, Reinforcement learning and Collaborative filtering. The end of the day they will able to apply machine learning algorithms to solve real-world problems.

Expected learning outcomes

Understand how machine can learn the concepts
Significant exposure to real-world implementations
To develop research interest in the theory and application of machine learning

Programming related learning outcomes

The course include a significant amount of programming in Python, which the student needs to master during the labs and project.
Each student team will develop a project written in Python with the help of machine learning libraries.

Required background knowledge

An overview of artificial intelligence fundamentals and Python programming skills would be a plus point, but not required.

Prerequisite courses

As a graduate level course, the students are expected to have engineering undergraduate background. Familiarity with basics concepts of probability, linear algebra and basics of machine learning.

Detailed topics covered in the course

Introduction to Advanced Machine Learning
Neural Network Architectures: CNN and Training Nets
Recurrent Neural Networks, LSTM, Attention
Splines and GAMs
Evolutionary Computations – Genetic Algorithms
Bayesian Learning – Bayes Theorem and Bayes Network
Graphical Models – Hidden Markov Model
Autoencoders – Variational autoencoder, inference, applications
Collaborative Filtering
Generative Models – GAN
Reinforcement Learning – Q learning, deep learning

Textbook

Handouts supplied by the instructor
Materials from the interment and research papers shared by instructor

Reference material

Required computer resources

Student should bring laptop machine in the class.

Evaluation

Quiz (10%)
Group project (20%)
Lab participation (20%)
Mid-term exam + Lab (20% + 5%)
Final exam + Lab (20% + 5%)

MSc:AdvancedMachineLearning old

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