Course Name
|
Turkish |
İşletme Uygulamaları ile Makine Öğrenmesine Giriş
|
| English |
Introduction to Machine Learning with Business Applications |
Course Code
|
ISL 439E |
Credit |
Lecture
(hour/week) |
Recitation
(hour/week) |
Laboratory
(hour/week) |
| Semester |
1
|
3 |
3 |
- |
- |
| Course Language |
English |
| Course Coordinator |
Tolga Kaya
|
| Course Objectives |
1. to introduce students several fundamental concepts and methods for machine learning.
2. to familiarize the audience with learning algorithms and their applications in business environments.
3. to develop an understanding of the modern data analysis, strengths and weaknesses of many popular machine learning approaches.
|
| Course Description |
Introduction to machine learning, applications of machine learning, introduction to programming with R, supervised and unsupervised learning, linear regression and its extensions, classification overview, logistic regression, linear discriminant analysis, using Bayes’ Theorem for classification, Naive Bayes technique, K-nearest neighbors approach, resampling methods, cross validation, Ridge regression, Lasso regression, tree based methods, random forest technique, boosted regression and classification, support vector machines, neural networks, an overview of deep learning, unsupervised learning methods, principal components analysis, K-means clustering, hierarchical clustering, Apriori algorithm, market basket analyses.
|
| Course Outcomes |
Students who pass the course will be able to,
1- have a good understanding of the basic concepts, fundamental problems and limitations of machine learning.
2- understand the mathematical models and the assumptions behind various supervised and unsupervised learning algorithms.
3- conduct exploratory analysis, visualize and manipulate data effectively in R programming environment.
4- implement and apply various machine learning techniques in a range of real world business applications.
5- compare the performances of alternative learning algorithms using appropriate tools and criteria. |
| Pre-requisite(s) |
You should understand basic probability and statistics, and college-level algebra and calculus. For example, it is expected that you know about standard probability distributions and also how to calculate derivatives. Although the knowledge of linear algebra is beneficial, you will have opportunity to learn basic mathematics underlying probability models. It would be beneficial to have some background in programming. Any experience in data management by elementary R, Python, Matlab, or Stata would also be helpful. |
| Required Facilities |
Participants are expected to bring a notebook (or a netbook) to the classes when requested. |
| Other |
R programming language will be used in the labs and exercises. |
| Textbook |
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. New York: Springer.
Lantz, B. (2015). Machine learning with R. 2nd Edition. Packt Publishing Ltd.
Grinberg, N. F.,Reed, R. J. (2013) Programming exercises for R. Warwick University. |
| Other References |
Alpaydin, E. (2014). Introduction to machine learning. MIT press.
Nielsen, M. A. (2015). Neural networks and deep learning. Determination Press.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning. Cambridge: MIT press.
Jockers, M. L. (2014). Text analysis with R for students of literature. New York: Springer.
Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning: Data Mining, Inference, and Prediction, 2nd Edition. New: Springer.
MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge University Press, York. |
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