Lecturer: Prof. Wang Jian-Sheng
Schedule/Venue: tba
Final exam: tba
reference books: "Deep Learning", Goodfellow, Bengio, and Courville; "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, basic concepts machine learning
Week 4: Feb, pytorch examples, linear regression as machine learning
Week 5: Feb, perceptrons, feedforward network
Week 6: Feb, gradient descent, backprogation
recess week, no classes
Week 7, Mar, convolutional network (CNN)
Week 8: Mar, Stochastic gradient descent and other optimization algorithms
Week 9: Mar, Long Short-Term memory?
Week 10: Mar, Hopfield network?
Week 11: Apr, unsupervised learing (Boltzmann machine)
Week 12: Apr, encoder-decoder
Week 13: Apr, transformer?
Homework problem sets are on Canvas at Files. Upload homework as PDF on Canvas at Assignments.