for the presentation part of course im not expecting you to send me a video of you doing the presentation, but I would like notes or a script so that I can have to help with presenting. I would have to be presenting for 15 min on video Recommended Materials
- Lecture handouts/slides (provided)
- Bontempi, G., & Taieb, S. B. (2017), Statistical foundations of machine learning (Links to an external site.). Universite Libre de Bruxelles.
- Tan, P. N., & Steinbach, M., & Kumar, V., Introduction to Data Mining (Links to an external site.), Pearson Education, Inc (1st or 2nd edition).
- Mitchell, T. (1997), Machine Learning (Links to an external site.). McGraw Hill.
- Murphy, K. P. (2012). Machine learning: a probabilistic perspective (Links to an external site.). MIT Press.
- Tipping, Michael E. “Bayesian inference: An introduction to principles and practice in machine learning (Links to an external site.).” Summer School on Machine Learning. Springer, Berlin, Heidelberg, 2003.
Optional Materials
- Montgomery, D. C., & Runger. G. C. (2010), Applied statistics and probability for engineers (Links to an external site.). JohnWiley & Sons.
- Bishop, Christopher (2006). Pattern Recognition and Machine Learning (Links to an external site.). Springer-Verlag New York.
- Alpaydin, Ethem. (2014). Introduction to machine learning (Links to an external site.). MIT press (3rd edition).
Course Learning Objectives:
Following this course, students will be able to:
- Use different estimation approaches including least squares, maximum likelihood, and maximum a posterior to estimate unknown parameters in a function or model.
- Differentiate supervised and unsupervised learning problems and choose proper approaches to solve these problems.
- Construct and implement linear regression, logistic regression, neural network models from real-life application datasets programmatically using Matlab or Python, and use the models to perform predictions.
- Construct decision trees using different methods for different types of data.
- Identify suitable clustering techniques for different data types, use these techniques to perform clustering, and validate the clustering results using different methods.
- Explain different dimension reduction methods and apply principal component analysis to reduce the dimension of a dataset.
- Identify and formulate a real-life application problem, develop and implement proper machine learning algorithms to solve the problem, assess the performance of the proposed algorithm, report and present the results.
Course Description:
Machine learning, a discipline that deals with the automatic design of models from data, has been successfully used in the past few decades for data analysis, process automation, function approximation, model building, and many others. These techniques have been explored in a diversity of fields such as robotics, self-driving cars, big data, control of autonomous systems, image analysis, object recognition, data mining, business, and financial forecasting, transportation systems, antenna design, medical care systems, and many others. It is believed by many researchers that machine learning is the best way to enable human-level artificial intelligence.
This course provides a broad introduction on the key machine learning techniques that are frequently used in the Engineering field. The main topics to be covered include probability theory, point estimation, linear regression, logistic regression, neural networks, decision trees, clustering, Bayesian estimation, and dimension reduction. In this course, students will not only learn these machine learning techniques, but also gain practice implementing them using programming tools such as Matlab, Python or R. Students will also explore real-life applications of machine learning and learn to solve the application problem using machine learning techniques.
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