Machine learning focuses on developing computational techniques that enable systems to learn from data, recognize patterns, and extract meaningful insights. These insights can be used to generate predictions, make informed decisions, or uncover hidden structures within data. The course covers fundamental concepts, methodologies, and practical applications of machine learning, equipping students with the skills needed to design and implement intelligent systems.
Upon successful completion of this course, students will:
- Understand the principles of machine learning and its real-world applications. They will also be familiar with key concepts such as data modeling, overfitting, underfitting, generalization, memorization, training data, and validation data.
- Gain knowledge of supervised learning algorithms, their types, and practical applications.
- Be able to apply various regression techniques and neural networks to uncover hidden relationships in data.
- Understand probabilistic models and Bayesian-based machine learning algorithms.
- Recognize the importance of data quality in machine learning and learn techniques for data cleaning and preprocessing.
- Be able to explain and implement classification algorithms in basic scenarios.
- Develop an understanding of unsupervised learning concepts, including clustering techniques.
- Define reinforcement learning and differentiate it from supervised and unsupervised learning.
- Be aware of the applications and limitations of machine learning algorithms.
- Develop essential skills such as lifelong learning, critical and analytical thinking, problem modeling and solving, IT proficiency, and decision optimization. Additionally, the course fosters oral, written, and interpersonal communication skills through group work and discussions in English. Ethical considerations, big data handling norms, and data protection protocols are also integrated into the learning process.
- Develop an open-minded approach to exploring diverse applications of machine learning as a data-driven system, recognizing its impact across various domains and industries.
