Course: Introduction to Learning Systems and Data Science
This course, part of the curriculum for aspiring data scientists and AI practitioners, offers a foundational understanding of key concepts and methodologies in learning systems and data science. The primary focus is on learning systems, their architectures, and how they can be effectively designed and implemented to solve complex problems.
Students will begin with Linear Algebra and Statistics for Data Science, which are crucial for understanding data structures and algorithms used in learning systems. This mathematical foundation will support later explorations into model evaluation and performance assessment.
The course will comprehensively cover Optimization Techniques for Learning, where students will learn how to enhance learning systems through parameter tuning and various optimization algorithms. Understanding how to optimize models is essential for building high-performing learning systems that can adapt over time.
In addition to foundational concepts, participants will gain insights into the Fundamentals of Machine Learning, focusing on how different algorithms can be applied to create learning systems that make predictions and automate decision-making processes. The curriculum will also explore Neural Networks and Deep Learning, emphasizing their pivotal role in the development of advanced learning systems capable of handling intricate tasks, such as image and speech recognition.
The course will introduce Natural Language Processing and Language Models, which will allow students to examine how learning systems can understand and generate human language. This segment will also cover practical applications, such as chatbots and sentiment analysis tools.
An important aspect of the course will be Hybrid Learning Approaches, highlighting how different machine learning paradigms (e.g., supervised, unsupervised, and reinforcement learning) can be combined to build more robust and flexible learning systems.
Human-centred Design principles will be emphasized to ensure that learning systems are intuitive, user-friendly, and effectively meet user needs. The course will also delve into the ethical implications of developing and deploying learning systems, covering considerations such as bias, fairness, and transparency in AI.
Practical Examples will serve as a capstone to the course, where students will engage in hands-on projects that reinforce learning. These projects will allow participants to apply the theoretical concepts learned throughout the course to build real-world learning systems that can analyze data, make decisions, and interact with users effectively.
Course Topics
- Linear Algebra and Statistics for Data Science
- Optimization Techniques for Learning
- Fundamentals of Machine Learning
- Neural Networks and Deep Learning
- Natural Language Processing and Language Models
- Introduction to Hybrid Learning Approaches
- Human-centred Design
- Ethics and Deployment in Learning Systems
- Practical Examples
Course Schedule
| Lecture | Date | Lecture Title | Lecture + |
|---|---|---|---|
| Lecture 1 | 30.10.25 | Course Intro | |
| Lecture 2 | 06.11.25 | Mathematical Cores | |
| Lecture 3 | 13.11.25 | Mathematical Cores | |
| Lecture 4 | 20.11.25 | Mathematical Cores | Pytorch |
| Lecture 5 | 27.11.25 | Optimization Techniques for Learning | |
| Lecture 6 | 04.12.25 | Introduction to Machine Learning | Advanced Optimization |
| Lecture 7 | 11.12.25 | Introduction to Machine Learning | NLP Cores |
| Lecture 8 | 18.12.25 | Deep Learning | |
| Christmas | 25.12.25 | ||
| New Year’s Day | 01.01.26 | ||
| Lecture 9 | 08.01.26 | Deep Learning | |
| Lecture 10 | 15.01.26 | Natural Language Understanding | Medical Imaging & RL |
| Lecture 11 | 22.01.26 | Natural Language Understanding | |
| Lecture 12 | 29.01.26 | Misc, Recap | |
| Exam | 05.02.26 |