Machine Learning and Deep Learning for Data Science
(데이터사이언스를 위한 머신러닝 딥러닝)
Spring 2020(Tue/Thu 15:00~16:15)
Instructor: Dr. Wen-Syan Li (wensyanli@snu.ac.kr, wensyanli@hotmail.com), Office 412
TA : Park YoungJoon(dudwns930@snu.ac.kr)
Spring 2020(Tue/Thu 15:00~16:15)
Instructor: Dr. Wen-Syan Li (wensyanli@snu.ac.kr, wensyanli@hotmail.com), Office 412
TA : Park YoungJoon(dudwns930@snu.ac.kr)
By the end of this class, students will learn the main concepts, methodologies, and tools for machine learning and deep learning be able to recognize machine learning tasks in real-world problems develop the critical thinking to analyze a given task and perform model selection and evaluation. Students will also gain the experience of applying the data science process end-to-end as an individual and as a team member.
Part I. Machine Learning
1. Introduction of Machine Learning, family of algorithms, supervised learning, unsupervised learning, deep learning,
2. Linear Regression (Model Representation, Cost Function, Gradient Descent)
3. Linear Algebra Review / Linear Regression with Multiple Variables, Multiple Features
4. Linear Regression with Multiple Variables, Multiple Features, Some Useful Practices
5. Logistic Repression (classification, Hypothesis Representation, Decision Boundary, Cost Functions)
6. Logistic Repression (classification, Hypothesis Representation, Decision Boundary, Cost Functions) / Tools for Machine Learning
7. Regularization (overfitting,, underfitting, cost function, regularized regression algorithms)
8. Neural Network (Representation, learning, back propagation, reinforced learning, random initialization, etc.)
9. Neural Network (how to put everything together, examples, ) / Generic Algorithms
10. SVM / Clustering
11. Feature Reduction / Machine Learning System Design / Case study
Part II. Deep Learning
12. Deep Feedforward Networks / Regulation and Training for Deep Learning /
13. Convolutional Networks / Sequence Modeling / Practical Methodology / Applications/ Case Study
Part III. Deep Learning Research
14. Linear Factor Models / Autoencoders / Reprepresentional Learning / Monte Carlo Methods / etc.
Part IV. Project Presentation
15. Summary of the course and future research topics / project presentation
Familiarity with Python, R, or MATLAB is needed for programming-based assignments. A good reference is the Python Data Science Handbook by Jake VanderPlas. Students are encouraged to go through the book or on line before starting the class.
There is no single required textbook for this course as the lectures will be based on multiple textbooks, various articles, and web documents as well as real scenarios from external companies. Among numerous textbooks available in the market, the following are recommended.
Pattern Recognition and Machine Learning (Information Science and Statistics)by Christopher M. Bishop, ISBN-13:978-0387310732. On line material and downloadable pdf are available at https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/
Machine Learning by Tom M Mitchell ISBN-13: 978-1259096952. On line material is available at http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville, ISBN-13:978-0262035613. On line material is available at https://www.deeplearningbook.org/
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) second edition by Richard S. Sutton(Author), Andrew G. Barto, ISBN-13: 978-0262039246. On line material is available at https://mitpress.mit.edu/books/reinforcement-learning-second-edition (reinforcement learning focused book)
Machine Learning by Andrew Ng’s online machine learning course available at https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN
This course will be taught in English. All lectures as well as exams and assignments will be given in English. Students will use English for answering exam questions and doing assignments.