## CS182: Introduction to Machine LearningProf. Ziping Zhao, ShanghaiTech University, Fall Term 2021-22.
## Course Descriptions
## AnnouncementsPiazza: https://piazza.com/shanghaitech.edu.cn/fall2021/cs182 Gradescope: https://www.gradescope.com/courses/324113
## PrerequisitesCompulsory: Linear Algebra, Mathematical Analysis or Advanced Calculus, Probability and Statistics, Programming. Recommended Postrequisites: SI231 Matrix Analysis and Computations, SI251 Convex Optimization, CS282 Machine Learning, CS280 Deep Learning, SI252 Reinforcement Learning.
## Textbooks and Optional References## TextbooksEthem Alpaydin, *Introduction to Machine Learning (4th Edition)*, The MIT Press (ACML book series), 2020.Kevin P. Murphy, *Machine Learning: A Probabilistic Perspective*, The MIT Press (ACML book series), 2012.Christopher Bishop, *Pattern Recognition and Machine Learning*, Springer, 2006.Tom Mitchell, *Machine Learning*, McGraw Hill, 1997.
## ReferencesMatthew F. Dixon, Igor Halperin, and Paul Bilokon, *Machine Learning in Finance*, Springer, 2020.Marcos Lopez de Prado, *Advances in Financial Machine Learning*, Wiley, 2018.Richard O. Duda, Peter E. Hart, and David G. Stork, *Pattern Classification (2nd Edition)*, Wiley, 2000.Trevor Hastie, Robert Tibshirani, and Jerome Friedman, *The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition)*, Springer, 2009.Zhihua Zhou, *Machine Learning*, Tsinghua University Press, 2016. (a ref. in Chinese)Hang Li, *Statistical Learning Methods (2nd Edition)*, Tsinghua University Press, 2019. (a ref. in Chinese)Shai Shalev-Shwartz and Shai Ben-David, *Understanding Machine Learning: From Theory to Algorithms*, Cambridge University Press, 2014.Aaron Courville, Ian Goodfellow, and Yoshua Bengio, *Deep Learning*, The MIT Press (ACML book series), 2016.Richard S. Sutton and Andrew G. Barto, *Reinforcement Learning: An Introduction (2nd Edition)*, The MIT Press (ACML book series), 2018.
## AI Labs## ML Practice Platform## Schedule (Subject to Change)Topics Topic 0: Overview Topic 1: ML Introduction; Mathematical Foundations of ML (Linear Algebra, Probability Theory, Optimization Theory, Information Theory, etc.) Topic 2: Bayesian Decision Theory Topic 3: Parameter Estimation for Generative Models Topic 4: Linear Discrimination Models Topic 5: Feedforward Neural Networks Topic 6: Support Vector Machines Topic 7: Model Assessment and Selection Topic 8: Deep Learning Models Topic 9: Recurrent Neural Networks Topic 10: Clustering and Mixture Models Topic 11: Nonparametric Methods Topic 12: Decision Trees Topic 13: Dimensionality Reduction Topic 14: Matrix Factorization Topic 15: Probabilistic Graphical Models Topic 16: Ensemble Learning Topic 17: Reinforcement Learning Guest Lecture: Deep Reinforcement Learning and Game AI [slides] Topic 18: Review
Selected Topics S-Topic 1: Probabilistic Graphical Models S-Topic 2: Probabilistic Topic Models S-Topic 3: Hidden Markov Models S-Topic 4: State Space Models S-Topic 5: Reinforcement Learning S-Topic 6: Numerical Optimization in ML
## 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. |