In this course, we will cover fundamental aspects of Machine Learning. We will start with fundamentals of machine learning, including different learning paradigms, regression and classification problems, evaluation methods, generalization and overfitting. We will then cover some of the fundamental machine learning techniques such as decision trees, Bayesian approaches, Naive Bayes classifier, and logistic regression, k-Nearest neighbor, and online learning algorithms. Besides understanding the basic theory behind the techniques, students are expected to apply them on different platforms like Weka or Matlab.
Machine Learning I (DA 514)
2021 Spring
Faculty of Engineering and Natural Sciences
Data Analytics(DA)
3
6
Hasan Sait Ölmez olmez@sabanciuniv.edu,
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Doctoral, Master
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CONTENT
PROGRAMME OUTCOMES
1. Develop the ability to use critical, analytical, and reflective thinking and reasoning
2. Reflect on social and ethical responsibilities in his/her professional life.
3. Gain experience and confidence in the dissemination of project/research outputs
4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments.
5. Communicate effectively by oral, written, graphical and technological means and have competency in English.
6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating.
1. Design and model engineering systems and processes and solve engineering problems with an innovative approach.
2. Establish experimental setups, conduct experiments and/or simulations.
3. Analytically acquire and interpret data.
1. Comprehend the conceptual foundations of analytical methods and techniques within the scope of business analytics,
2. Acquire theoretical and practical knowledge on applied information systems by developing fundamental programming skills,
3. Improve decision making by turning high-volume data into useful information and integrating data analysis tools
4. Turn high-volume data into useful information by using quantitative models and understanding and managing data analysis techniques, communicate and visualize the results for business use
5. Understand the data quality, data integrity and data accuracy concepts, and occupational ethics regarding data privacy and intellectual property