Machine Learning

• Textbook: 

  • 1.  Introduction to Machine Learning, 4th edition

by  E. Alpaydin,
The MIT Press, 2020.

• • 2. Understanding Deep Learning
    by Simon J.D.Prince,
    MIT Press, 2023. [Link] 

• Main Topics:

  • 1. Fundamentations

  • 2. Supervised Learning

  • 3. Unsupervised Learning

  • 4. Deep Learning

    • Convolutional networks, Residual networks, Transformers, Graph neural networks, Generative Adversarial networks,  Variational autoencoders, Diffusion models, etc.......

  • 5. Direct Related Topics:

    • Design and analysis of machine learning experiments, Bayesian decision theory, Parametric methods, Multivariate methods, Dimensionality reduction, Clustering, Decision trees, Linear discrimination, Kernel machines, etc. ......

• Main References:

  • Probabilistic Machine Learning: An Introduction,
    by Kevin P.Murphy, MIT Press, 2022. [Link]

  • Dive into Deep Learning
    by Aston Zhang, Zachary C. Lipton, Mu Li, & Alexander J. Smola.  2023.
    [Link]

• Evaluation:

Homework (& Project) 40%,
Midterm and Final Exam (30+30)%

• This is an EMI course.

• More References:

  • Pattern Recognition and Machine Learning,
    by C. M. Bishop, Springer, 2006. [Link]

  • Deep Learning,
    by Ian Goodfellow, Yoshua Bengio, and Aaron Courville,
    The MIT Press, 2016. [Link]

  • Machine Learning: An Algorithmic Perspective, 2nd edition
    by Stephen Marsland, CRC Press, 2015. 

  • Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow:
    Concepts, Tools, and Techniques to Build Intelligent Systems, (3/e),
    by Aurélien Géron, O'Reilly Media, 2022. 

  • Python Machine Learning:
    Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, (3/e),
    by Sebastian Raschka, Vahid Mirjalili, Packt Publishing, 2019.