The Machine Learning Course Syllabus is prepared keeping in mind the advancements in this trending technology. 2nd Edition, Springer, 2009. For extreme personal issues only: Rui Chen • Sheng Xu • Victor Arsenescu • Xi Chen • Xiaohui Chen • Lily Zhang • Zhitong Zhang. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. After you have spent at least 10 minutes thinking about the problem on your own, you may verbally discuss assignments with others in the class. Participation is not only required, it is expected that everyone in the course is treated with dignity and respect. / Can we find lower-dimensional representations of each example that do not lose important information? If in doubt, make it private. This meeting will happen by default in person (but only in a setting where it is safe to do so). Respect is demanded at all times throughout the course. Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. At each step, get practical experience by applying your skills to code exercises and projects. MIT Press, 2015. Any rounding up will be at the instructor's discretion, as will the highest possible grade of "A+". It allows us to always release homework solutions on Monday mornings a few days before the required quiz on that unit is due, and lets us discuss the assignment in class on Monday afternoon without issue. Fairness in Machine Learning (PA3 Review) ... Richard S. Sutton and Andrew G. Bart, Reinforcement Learning: An Introduction. Source on github releasing that video within 24 hours to the Piazza resources page. Compare and contrast evaluation methods for various predictive tasks (including receiver operating curves, precision-recall curves, and calibration plots). We will drop the lowest quiz grade (so only 4 of 5 quizzes will count to final grade). Each week, you should expect to spend about 10-15 hours on this class. Weekly recitation sessions will help students put key concepts into practice. Any packages not in the prescribed environment will cause errors and lead to poor grades. With these goals in mind, we have the following policy: Each student will have 192 total late hours (= 8 late days) to use throughout the semester across all homeworks. It gives an overview of many concepts, techniques and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such support vector machines. If you feel uncomfortable talking to members of the teaching staff, consider reaching out to your academic advisor, the department chair, or your dean. After the first day, we will expect students to be signed up on Piazza (accessible to any student either enrolled or on the waitlist). Introduction to Machine Learning. The timestamp recorded on Gradescope will be official. Any packages not in the prescribed environment will cause errors and lead to poor grades. Syllabus Introduction to Machine Learning Fall 2016 The course is a programming-focused introduction to Machine Learning. Projects turned in up to one week after the posted due date will be eligible for up to 90% of the points. This is supposed to be the first ("intro") course in Machine Learning. Date Lecture Topics Readings and useful links Anouncements; Module 1: Supversived Learning: Thu 9/3: Please see the detailed accessibility policy at the following URL: After completing this course, students will be able to: Programming: Students should be comfortable with writing non-trivial programs (e.g., COMP 15 or equivalent). Design and implement an effective solution to a regression, binary classification, or multi-class classification problem, using available open-source libraries when appropriate and writing from-scatch code when necessary. Quizzes assess what you as an individual understand about the course material. Introduction to Machine Learning Inductive Classification Decision-Tree Learning Ensembles Experimental Evaluation Computational Learning Theory Rule Learning and Inductive Logic Programming If you have concerns about your computing resources being adequate (see Resources page for expectations), please contact the course staff via Piazza ASAP. Introduction to Machine Learning CMSC422 University of Maryland. This late work deadline is key to our classroom goals. Tufts and the instructor of COMP 135 strive to create a learning environment that is welcoming students of all backgrounds and abilities. When using the Piazza forum, you should be aware of the policies previously mentioned while post posting questions and providing answers. How can a machine learn from experience, to become better at a given task? Please be aware that accommodations cannot be enacted retroactively, making timeliness a critical aspect for their provision. https://students.tufts.edu/student-affairs/student-life-policies/academic-integrity-policy, Tufts and the instructor of COMP 135 strive to create a learning environment that is welcoming students of all backgrounds. PDF writeups and Python code will be turned in via Gradescope. ... the instructor reserves the right to change any information on this syllabus or in other course materials. For extreme personal issues only: Mike Pietras • Rui Chen • Manh (Duc) Nguyen • Minh Nguyen • Yirong (Wayne) Tang. If you see any material having the same problem and providing a solution, you cannot check or copy the solution provided. Topics include linear models for classification and regression, support vector machines, regularization and model selection, and introduction to structured prediction and deep learning. Each student is responsible for shaping this environment: please participate actively and respectfully! Due to the large class size and the need to keep our whole community safe, most interactions will be virtual, including all in-class sessions and most office hours. Participation in class is strongly encouraged, as you will get hands-on practice with material and have a chance to ask questions of the instructor and TAs, as well as your peers. Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. WHY: Our goal is to prepare you to effectively apply machine learning methods to problems that might arise in "the real world" -- in industry, medicine, education, and beyond. Prof. Mike Hughes will make the final decision about all wait list candidates by end of day on Monday 9/21 (just before the ADD deadline), which is when the first homework will be turned in and fully graded. Students with unforeseen and exceptional circumstances may contact the instructor to make other arrangements (likely in the form of a makeup oral exam). Lecture Slides . family emergency, medical emergency). You will apply this knowledge by identifying different components essential to a machine learning business solution. Only a one time 1-on-1 meeting will be in person, with accomodations possible (more info below). 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