Fundamental Machine Learning
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What I will learn?
Learning the basics of ML
Learning the theories behind ML, its concepts, and algorithms
Learning the obstacles of ML algorithms and overcoming them
Building ML models, evaluating them, and analyzing results
INTRODUCING MACHINE LEARNING AND 2 METHODS OF SUPERVISED LEARNING
Introduction of Machine Learning
PRESENTING ML OBSTACLES AND DIFFERENT WAYS OF ADDRESSING THEM
Problem of over/undertraining
PRESENTING NEURAL NETWORKS AND THEIR LEARNING METHODS
PRESENTING KERNEL METHODS
INTRODUCES UNSUPERVISED LEARNING, ITS DIFFERENT ALGORITHMS, AND ITS PERFORMANCE EVALUATION
PRESENTING COMPUTATIONAL LEARNING THEORY AND MODEL EVALUATION METHODS
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Video Lectures: Presenting Concepts, Algorithms, and Mathematics as the basis of Machine learning methods.
Reading Materials (Slides)
Virtual lab Projects (assignments):
Problem representation: representation of a (real-world) problem.
Hands-on (Python) experience: executing Machine Learning methods on the problem and analyzing results.
Quiz: to ensure the understanding of students, after each chapter, a few questions (objective questions or short answer questions) are designed based on the content of the materials.
Statistics and Linear Algebra
Basic knowledge of Python programming
Access to the Anaconda environment
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