MACHINE LEARNING

Machine learning concerns with the design and development of algorithms that allow computers to improve their performance over time based on data, such as from sensor data or databases. We investigate some major machine learning paradigms: supervised learning, unsupervised learning, and statistical learning.

Understand the different fields of machine learning, such as supervised and unsupervised learning and reinforcement learning. Acquire theoretical Knowledge on setting hypotheses for pattern recognition. Apply suitable machine learning techniques for data handling and gain knowledge from it. Evaluate the performance of algorithms and provide solutions for various real-world applications. Learn how to use the Python programming language and Python’s scientific computing stack for implementing machine learning algorithms. Reinforce the learned knowledge; students will do some team projects in groups. Through these projects, students will solve some real-life or synthesized problems using some of the learned methods.

#1 When speaking in class, please identify yourselves #2 Ask questions (There are no “stupid” questions. If you don’t understand it, someone else probably doesn’t it, either.) #3 Help each other! [(Even when a project or assignment is specified as an individual, ask your friends or classmates about stuff you don’t understand.) >> When you have the answer, write it in your own words (or your own coding style).] #4 ALWAYS BE ON TIME #5 focus in the classroom

1. Ethem Alpaydin, ”Introduction to Machine Learning”, MIT Press, Prentice Hall of India, 3rd Edition2014. 2. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar ” Foundations of Machine Learning”, MIT Press,2012. 3. Tom Mitchell, “Machine Learning”, McGraw Hill, 3rdEdition, 1997. 4. MACHINE LEARNING - An Algorithmic Perspective, Second Edition, Stephen Marsland, 2015. 5. Python Machine Learning: Machine Learning and Deep Learning with Python (2019). Book Title: Python Machine Learning: Machine Learning and Deep Learning with Python (3rd Edition) Authors: Sebastian Raschka & Vahid Mirjalili ISBN-10: 1789955750 ISBN-13: 978-1789955750 Language: English

1. Recognize the characteristics of Machine Learning techniques that enable to solve real-world problems 2. Recognize the characteristics of machine learning strategies 3. Apply various supervised learning methods to appropriate problems 4. Identify and integrate more than one technique to enhance the performance of learning 5. Create probabilistic and unsupervised learning models for handling unknown pattern 6. Analyze the co-occurrence of data to find interesting frequent patterns