Lecturer: Prof. Wang Jian-Sheng
Schedule/Venue: Tuesday LT20/Friday LT28 2:00-4:00.
Final exam: tba
reference books: "Deep Learning, Foundations and Concepts", C. M. Bishop; "Deep Learning", Goodfellow, Bengio, and Courville; "Machine Learning", Lindholm, et al; "Neural Networks", Haykin.
Offical Syllabus: This course presents the mathematical and computational foundations of machine learning with an emphasis on deep learning networks, preparing the students with sufficient background for more advanced topics such as AI in physics or any of the other sciences. The learning outcomes include sufficient familiarity with the programming environment for machine learning with Python, a deeper understanding of the building blocks of neural networks, and numerical training algorithms for machine learning. The course will draw applications in science as examples to illustrate the concepts of deep learning.
Course Outline:
Week 1: Jan, biological neurons and artifical neuron networks
Week 2: Jan, python, numpy, pytorch
Week 3: Jan, linear regression, maximum likehihood, cross-validation
Week 4: Feb, perceptrons, feedforward network, backprogation
Week 5: Feb, Stochastic gradient descent and other optimization algorithms
Week 6: Feb, convolutional network (CNN)
recess week, no classes
Week 7: Mar, ?
Week 8: Mar, ?
Week 9: Mar, Long Short-Term memory?
Week 10: Mar, encoder-decoder
Week 11: Apr, transformer?
Week 12: Apr, generative model?
Week 13: Apr, unsupervised learning (Boltzmann machine)?
Homework problem sets are on Canvas at Files. Upload homework as PDF on Canvas at Assignments.