30562 - MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

- Basic concepts in machine learning - Programming in Python, including Numpy - Programming in C

The mission of the course is to introduce the students to state-of-the-art approaches of modern artificial intelligence. The course will focus on deep learning architectures at the frontier of current research and applications, including image classification, image generation, speech processing, translation, recommender systems, reinforcement learning, AI in healthcare and biology and language modelling.

1. Introduction to AI and Machine Learning
2. Deep learning systems and examples of state-of-the-art capabilities in different domains
3. Basic concepts around artificial neural networks
4. CUDA programming, PyTorch and JAX
5. ML Tools: IDEs, SSH, Tmux, Git, Tensorboard
6. Architectures and implementations for
   • Image Classification and Generation
   • Speech Recognition
   • Biological Sequence Analysis
   • Translation
   • Language Modelling
   • Reinforcement Learning
7. Unresolved issues in deep learning and outlook

- Describe how state-of-the-art deep learning systems work

- Reproduce the results of state-of-the-art deep learning systems (assuming the computational resources are available)

- Select a sensible architecure when faced with a new problem

- Estimate the computational resources necessary for training and deploying these models

- Identify shortcomings, caveats and limits of state-of-the-art deep learning systems

- Illustate the pipeline used during training and deploying these models

- Formulate state-of-the-art models in code

- Evaluate current research literature and understand it

- Apply these models using appropriate tools

- Assess and validate the models

• Face-to-face lectures
• Exercises (exercises, database, software etc.)

Face-to-face lectures will introduce fundamental and advanced concepts around modern machine learning and artificial intelligence. During the exercises, the students will (under the guidance of the instructor) implement and train state-of-the-art models partially or fully, and also validate the quality of the trained models.

The final grade will be determined by a group project (50%) and the result of a written indvidiual exam (50%).

The group project will verify that the student is able to design, train and validate a machine learning approach to solve a concrete problem, using the concepts and architectures treated in class. The written individual exam will assess the students' understanding of the theoretical concepts treated in class and also their capability of  quantative reasoning around the models studied.