MATH 537 | Course Introduction and Application Information

• Prof. Dr. GÖZDE YAZGI TÜTÜNCÜ AŞÇI

• Prof. Dr. GÖZDE YAZGI TÜTÜNCÜ AŞÇI

• Arş.Gör. Merve KAHRAMAN ARİMAN

The students who succeeded in this course;

• Explain mathematical foundations of AI methods.
• Apply linear algebra techniques in data analysis and model construction.
• Use probabilistic and statistical concepts in ML models.
• Analyze optimization algorithms.
• Develop basic ML models and simple neural networks.
• Evaluate ethical and security risks of AI models.

Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. ISBN: 978-0134610993

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. ISBN: 978-0387310732

Blitzstein, J. K., & Hwang, J. (2019). Introduction to probability (2nd ed.). CRC Press. ISBN: 978-1138369917

Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press. ISBN: 978-0521833783

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. ISBN: 978-0262035613

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. ISBN: 978-0387848570

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer. ISBN: 978-1461471370

Jurafsky, D., & Martin, J. H. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (2nd ed.). Prentice Hall. ISBN: 978-0131873216

Strang, G. (2006). Linear algebra and its applications (4th ed.). Brooks/Cole. ISBN: 978-0030105678

Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv. https://arxiv.org/abs/1609.04747

Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press. ISBN: 978-0262018029

Vapnik, V. N. (1998). The nature of statistical learning theory (2nd ed.). Springer. ISBN: 978-0387987804

van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605

Nielsen, M. (2015). Neural networks and deep learning. Online book. http://neuralnetworksanddeeplearning.com

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://arxiv.org/abs/1706.03762

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. ISBN: 978-0553418811

McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (3rd ed.). O’Reilly Media. ISBN: 978-1098104030