## CS182: Introduction to Machine LearningProf. Ziping Zhao, ShanghaiTech University, Spring Term 2023-24.
## Course DescriptionsMachine learning (ML) is the science of making computer artifacts improve their performance without requiring humans to program their behavior explicitly. Machine learning has accomplished successes in a wide variety of challenging applications, ranging from computational molecular biology to computer vision to social web analysis.
## AnnouncementsPiazza: https://piazza.com/shanghaitech.edu.cn/spring2024/cs182 Gradescope: See the HW's.
## PrerequisitesCompulsory: Linear/Matrix Algebra, Mathematical Analysis or Advanced Calculus, Probability and Statistics, Programming. Recommended Postrequisites: Matrix Analysis and Computations, Convex Optimization, Machine Learning, Trustworthy Machine Learning.
## Textbooks and Optional References## TextbooksEthem Alpaydin, *Introduction to Machine Learning (4th Edition)*, The MIT Press, 2020.
## ReferencesRichard O. Duda, Peter E. Hart, and David G. Stork, *Pattern Classification (2nd Edition)*, Wiley, 2000.Christopher Bishop, *Pattern Recognition and Machine Learning*, Springer, 2006.Shai Shalev-Shwartz and Shai Ben-David, *Understanding Machine Learning: From Theory to Algorithms*, Cambridge University Press, 2014.Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, *Foundations of Machine Learning (2nd Edition)*, MIT Press, 2018.
## Schedule (Subject to Change)Topics Topic 0: Overview Topic 1: ML Introduction Topic 2: Mathematical Foundations of ML (Linear Algebra, Probability and Statistics, Optimization Theory, etc.) Topic 3: Bayesian Decision Theory Topic 4: Parameter Estimation for Generative Models Topic 5: Linear Discrimination Models Topic 6: Multilayer Perceptrons Topic 7: Support Vector Machines Topic 8: Dimensionality Reduction Topic 9: Clustering and Mixture Models Topic 10: Nonparametric Methods Topic 11: Deep Learning Models Topic 12: Ensemble Learning Topic 13: Model Assessment and Selection Topic 14: Review
## Assessment30% assignments, 40% final exam, 30% final project. ## Academic Integrity PolicyGroup study and collaboration on problem sets are encouraged, as working together is a great way to understand new materials. Students are free to discuss the homework problems with anyone under the following conditions: Students must write down their own solutions. Plagiarism is never allowed. Similar answers, MATLAB codes, etc., found in HWs will invite you into suspected plagiarism investigation. Students must list the names of their collaborators (i.e., anyone with whom the assignment was discussed). Students can not use old solution sets from other classes under any circumstances, unless the instructor grants special permission.
Students are encouraged to read the ShanghaiTech Policy on Academic Integrity. |