# introduction to machine learning syllabus

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Reinforcement Learning: How can an agent learn from interacting with an environment and receiving feedback about its actions? We understand some students are on the wait list (either formally on the wait list on SIS system, or just conceptually would like to be in the course). We are currently at capacity, but some students may drop the course and leave openings for others (usually we see 10-20 openings in the first week of classes as schedules shift). Machine learning is the science of getting computers to act without being explicitly programmed. However, we do encourage high-level interaction with your classmates. Please be aware that accommodations cannot be enacted retroactively, making timeliness a critical aspect for their provision. https://students.tufts.edu/student-accessibility-services. In class, we will typically have the following structure, all over Zoom: We will strive to create an exciting, highly interactive virtual classroom, with lots of opportunities for students to ask questions and get feedback from the professor, TAs, and peers. Jump to: After completing this course, students will be able to: As of the start of semester, we expect to have 120 students enrolled in the course. Splitting data between training sets and … We realize everyone comes from a different background with different experiences and abilities. Can we find lower-dimensional representations of each example that do not lose important information? These include textbook readings as well as watch prerecorded videos (posted to Canvas). Sci.) Naive Bayes. Some issues warrant public questions and responses, such as: misconceptions or clarifications about the instructions, conceptual questions, errors in documentation, etc. Please see the community-sourced Self-Study Resources Page for a list of potentially useful resources for self-study. Self-Study Resources Page for a list of potentially useful resources for self-study. You may work out solutions together on whiteboards, laptops, or other media, but you are not allowed to take away any written or electronic information from joint work sessions. Students are expected to finish course work independently when instructed, and to acknowledge all collaborators appropriately when group work is allowed. For each individual assignment (homework or project), you can submit beyond the posted deadline at most 48 hours (2 days) and still receive full credit. WHAT: How can a machine learn from data or experience to improve performance at a given task? 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). You may work out solutions together on whiteboards, laptops, or other media, but you are not allowed to take away any written or electronic information from joint work sessions with others. If you feel uncomfortable or unwelcome for any reason, please talk to your instructor so we can work to make things better. For each individual assignment, you can submit beyond the posted deadline at most 96 hours (4 days) and still receive full credit. Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. Before each class, you are expected to complete the "Do Before Class" activities posted on the Schedule. You may not share any written code or solutions with other students. 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. Please start early (at least 2 weeks before deadline) and make a careful plan with your group. Anna University. If you see any material having the same problem and providing a solution, you cannot check or copy the solution provided. A dictionary de nition includes phrases such as \to gain knowledge, or understanding of, or skill in, by study, instruction, or expe- : Key concepts for the day (instructor led), Next 35 min. Identify relevant real-world problems as instances of canonical machine learning problems (e.g. CSCI 467 Syllabus { August 26, 2019 5 Tentative Course Outline Monday Wednesday Aug 26th 1 Introduction to Statistical Learning (ISLR Chs.1,2, ESL Chs.1,2) Supervised vs. Unsupervised Learning 28th 2 Introduction to Statistical Learning (ISLR Chs.1,2, ESL Chs.1,2) Model Assessment Sep 2nd Labor Day 4th 3 Linear Regression (ISLR Ch.3, ESL Ch. Introduction to Machine Learning Course. ✨, COMP 135: Introduction to Machine Learning, Department of Computer Science, Tufts University, https://piazza.com/tufts/spring2019/comp135/home, https://github.com/tufts-ml-courses/comp135-19s-assignments, Elements of Statistical Learning: Data Mining, Inference, and Prediction, https://students.tufts.edu/student-affairs/student-life-policies/academic-integrity-policy, https://students.tufts.edu/student-accessibility-services, Lecture: Mon and Wed 3:00-4:15pm in Halligan 111A, Recitation Sessions (led by TAs): Mon 7:30 - 8:30 pm in Halligan 111B. We will gladly accommodate students who request a remote meeting, by holding the meeting over Zoom. If you are allowed to use a package, there are two caveats: Do not use a tool blindly: You are expected to show a deep understanding of any method you apply, as demonstrated by your writeup. Syllabus Introduction to Machine Learning Fall 2016 The course is a programming-focused introduction to Machine Learning. Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine Some issues are better with private posts, including: debugging questions that include extensive amounts of code, questions that reveal a portion of your solution, etc. Contact: Please use Piazza. Please refer to the Academic Integrity Policy at the following URL: Machine learning … / Late time is rounded up to the nearest hour. This CS425/528 course on Machine Learning will explain how to build systems that learn and adapt using real-world applications. 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. Increasingly, extracting value from data is an important contributor to the global economy across a range of industries. Beware of autograder requirements: If the problem requires you to submit code to an autograder, we will need to run the code using only the prescribed default software environment. [Overview] • [Prereqs] • [Deliverables] • [Collaboration-Policy]. This course provides an introduction on machine learning. PDF writeups will be turned in via Gradescope. Participation is not only required, it is expected that everyone in the course is treated with dignity and respect. Please use your best judgment when selecting private vs. public. Multiple choice questions will be evaluated by autograder on Gradescope, Short answer questions will be evaluated by TA graders, Makeup quizzes will not be issued except in cases of, 3 projects: open-ended programming challenges, Results and relevant code will be turned into Gradescope, Polished PDF reports will be turned in via Gradescope, An in-person meeting with course staff (with accommodations possible), Sign-up information and details will be posted by the end of September to Piazza, 1.25 hr / wk preparation before Mon class (reading, lecture videos), 1.25 hr / wk active participation in Mon class, 1.25 hr / wk preparation before Wed class (reading, lecture videos), 1.25 hr / wk active participation in Wed class, 3.00 hr / wk on homework (due every two weeks, so each hw takes 6 hr total), 4.00 hr / wk on project (due every four weeks, so each proj takes 16 hr total), 1.50 hr / wk preparing for quiz (quizzes happen every 2 weeks, so each quiz is 3 hr total), 22% average of homework scores (HW0 weighted 2%, HW1-HW5 weighted 5% each after dropping the lowest score), 40% average of quiz scores (Q1-Q5, weighted equally after dropping the lowest score), 36% average of project scores (ProjA, ProjB, and ProjC, weighted equally), 2% participation in the required meeting as well as in class and in Piazza discussions. Corrected 12th printing, 2017. INTRODUCTION TO MACHINE LEARNING Syllabus: CSC 311 Winter 2020 1. Compare and contrast evaluation methods for various predictive tasks (including receiver operating curves, precision-recall curves, and calibration plots). Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Please consult our Python Setup Instructions page to get setup a Python environment for COMP 135. Supervised Learning: Given a collection of inputs and corresponding outputs for a prediction task, how can we make accurate predictions of the outputs that correspond to future inputs? MIT License Our top priority is to provide each enrolled student with our full support, including the ability to get prompt answers to questions on Piazza and in office hours as well as the ability to get high-quality feedback on submitted homeworks, exams, and projects in a timely manner. sophomore undergraduate in CS, Ph.D. student in Cog. Please use your best judgment when selecting private vs. public. The objective of this class is to provide a rigorous training on the fundamental concepts, algorithms, and theories in machine learning. CS273A: Introduction to Machine Learning. After the due date, you can receive zero credit. Programming: Students should be comfortable with writing non-trivial programs (e.g., COMP 15 or equivalent). It is possible that students currently on the wait list may be added, but only if there is adequate staff support. Thus, for one assignment in the course due on Thu 9:00am ET, you could submit by the following Mon at 9:00am ET. If in doubt, make it private. UG Questions. SYLLABUS Intro to Machine Learning with PyTorch. : Recap of key concepts and lessons learned, Perform vector mathematical operations in. O'Reilly, 2015. For work that is intended to be done individually (homework), we interpret "others" as as anyone else, whether in the class or not. Please consult our Python Setup Instructions page to get setup a Python environment for COMP 135. See Piazza post on Required Office Hours visit for details about scheduling your appointment and signing the official log to get this counted. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Introduction: Welcome to Machine Learning and Imaging, BME 548L! Finally, open-ended practical projects -- often organized like a contest -- will allow students to demonstrate mastery. This deadline is key to our classroom goals. https://students.tufts.edu/student-affairs/student-life-policies/academic-integrity-policy. [Bishop] Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer. you are allowed to use. When preparing your solutions, you may always consult textbooks, materials on the course website, or existing content on the web for general background knowledge. 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. Evaluating Machine Learning Models by Alice Zheng. Unsupervised Learning: What are the major underlying patterns in a given dataset? By the first homework, students will be expected to do the following without much help: Midterm will be during a normally scheduled class period, Final will be at the appointed final exam hour and location for this class, Makeup exams will not be issued except in cases of, 8 homework assignments (written and code exercises). How can a machine learn from experience, to become better at a given task? If you feel uncomfortable talking to members of the teaching staff, consider reaching out to your academic advisor, the department chair, or your dean. ... Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press 2004. This class will provide a comprehensive overview of two major areas of machine learning: We will also provide some brief exposure to reinforcement learning. You will apply this knowledge by identifying different components essential to a machine learning business solution. Corrected 8th printing, 2017. Prior experience with linear algebra and probability theory will also be useful. Students are expected to finish course work independently when instructed, and to acknowledge all collaborators appropriately when group work is allowed. We will regularly use several textbooks available for free online (either in browser or via downloadable PDFs): There are several primary deliverables for students in the course: We want students to develop the skills of planning ahead and delivering work on time. you are allowed to use. Instructional material (readings, notes, and videos) will always be "prerecorded" and released on the Schedule page in advance, under "Do Before Class". Essential Mathematics background: Familiarity with multivariate calculus (esp. We will regularly use several textbooks available for free online (either in browser or via downloadable PDFs): There are three primary tasks for students throughout the course: Late work policy for homeworks and projects: We want students to develop the skills of planning ahead and delivering work on time. We do encourage high-level interaction with your classmates. However, the most valueable learning interactions may occur in breakout rooms that cannot be recorded. derivatives and vector derivatives) is essential. Our knowledge will always be used to better everyone in the class. Date Lecture Topics Readings and useful links Anouncements; Module 1: Supversived Learning: Thu 9/3: 1.1 Introduction 1.1.1 What is Machine Learning? With instructor approval, as long as you turn in high-quality work by the end of the semester, you can still earn up to 60% of the points. / How can we automatically extract knowledge or make sense of massive quantities of data? Design and implement effective strategies for preprocessing data representations, partitioning data into training and heldout sets, and selecting hyperparameters. Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. Students with unforeseen and exceptional circumstances may contact the instructor to make other arrangements (likely in the form of a makeup oral exam). Projects turned in by the posted due date will be eligible for up to 100% of the points. At each step, get practical experience by applying your skills to code exercises and projects. 2nd Edition, Springer, 2009. We will use Python, a popular language for ML applications that is also beginner friendly. We will drop the lowest quiz grade (so only 4 of 5 quizzes will count to final grade). The course covers the necessary theory, principles and algorithms for machine learning… / Please see the community-sourced Prereq. This is supposed to be the first ("intro") course in Machine Learning. We intend that students in this situation could still pass the course if needed. Complete and submit HW0 by end of day Wed 9/16. Jump to: Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Because many "solutions" are possible, we will strive to be flexible, while still incentivizing students to turn in high-quality work on time so we can grade in a timely manner. Emails, text messages, and other forms of virtual communication also constitute “notes” and should not be used preparing solutions. Optional Machine Learning Books [Murphy] Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press. Lecture Slides . Questions may be posted as either private (viewable only by yourself and course staff) or public (additionally viewable by all students for the course registered on Piazza). For work that is intended to be done on small teams (projects), we interpret "others" above as anyone not on your team. Some issues warrant public questions and responses, such as: misconceptions or clarifications about the instructions, conceptual questions, errors in documentation, etc. MIT Press, 2015. Quizzes CANNOT be turned in late. Critique core and cutting edge machine learning algorithms 2.Apply machine learning systems to perform various arti cial intelligence tasks. This action shows you have the necessary skills and would take the course seriously, Message the instructor by end of day Wed 9/16 via email with subject containing "COMP 135 Wait List Request", explaining your current state within the degree program (e.g. Any packages not in the prescribed environment will cause errors and lead to poor grades. / Design and implement an effective solution to a regression, binary classification, or multi-class classification problem. Lectures: 2 sessions / week, 1.5 hours / session A list of topics covered in the course is presented in the calendar. Instructor: Sargur Srihari Department of Computer Science and Engineering, University at Buffalo Machine learning is an exciting topic about designing machines that can learn from examples. Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. / 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. Use Naive Bayes with scikit learn in python. Freely available online. Finally, open-ended practical projects -- often organized like a contest -- will allow students to demonstrate mastery. Module 2 - Regression Linear Regression Non-linear Regression Model evaluation methods . If you have a disability that requires reasonable accommodations, please contact the Student Accessibility Services office at Accessibility@tufts.edu or 617-627-4539 to make an appointment with an SAS representative to determine appropriate accommodations. Here's our recommended break-down of how you'll spend time each week: Final grades will be computed based on a numerical score via the following weighted average: When assigning grades, the following scale numerical scale will be used: This means you must earn at least an 0.83 (not 0.825 or 0.8295 or 0.8299) to earn a B instead of a B-. This course will strictly follow the Academic Integrity Policy of Tufts University. This course will be an introduction to the design (and some analysis) of Machine Learning algorithms, with a modern outlook, focusing on the recent advances, and examples of real-world applications of Machine Learning algorithms. Projects are open-ended and involve working with peers on significant code implementation and written reports. Beyond your allowance of 192 late hours, zero credit will be awarded except in cases of truly unforeseen exceptional circumstances (e.g. Identify relevant ethical and social considerations when deploying a supervised learning or representation learning method into society, including fairness to different individuals or subgroups. This is because the syllabus is … 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. Beyond your allowance of late hours, zero credit will be awarded. : Course Announcements (instructor led), Next 25 min. On the other hand, we know that fall 2020 offers particular challenges, and we wish to be flexible and accommodating within reason. [Overview] • [Class-Format] • [Wait-List] • [Prereqs] • [Deliverables] • [Late-Work] • [Collaboration-Policy]. The Machine Learning Course Syllabus is prepared keeping in mind the advancements in this trending technology. PDF writeups and Python code will be turned in via Gradescope. Our ultimate goal is for each student to fully understand the course material. Useful Mathematics background: Prior experience with linear algebra and probability theory will also be useful. You are responsible for everything that you hand in. 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. Each synchronous class session will occur at the scheduled time (Mon and Wed from 430-545pm ET). Allowing lateness might encourage intentional or unintentional sharing of answers. Powered by Pelican Contact: Please use Piazza. Machine learning engines enable intelligent technologies … This class is an introductory undergraduate course in machine learning. Home Quick links Schedule Syllabus Topics. clustering, regression, etc.). O'Reilly, 2015. For example, if the assignment is due at 3pm and you turn it in at 3:05pm, you have used one whole hour. Supervised Learning: Given a set of inputs and outputs, how can we make predictions about future outputs? Some issues are better with private posts, including: debugging questions that include extensive amounts of code, questions that reveal a portion of your solution, etc. No notes, no diagrams, and no code. If you see any material having the same problem and providing a solution, you cannot check or copy the solution provided. With this goal in mind, we have the following policy: You must write anything that will be turned in -- all code and all written solutions -- on your own without help from others. You may not share any code or solutions with others, regardless of if they are enrolled in the class or not. To facilitate learning, we also want to be able to release solutions quickly and discuss recent assignments soon after deadlines. With these goals in mind, we have the following policy: Each student will have 120 total late hours (5 late days) to use throughout the semester across the 8 homeworks and 3 projects. These are the fundamental questions of machine learning, a growing field of knowledge that combines techniques from computer science, optimization, and statistics. family emergency, medical emergency). Machine Learning Course Syllabus. This late work deadline is key to our classroom goals. We do not require attendance at any class or track attendance. 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. With instructor permission, diligent students who are lacking in a few of these areas will hopefully be able to catch-up on core concepts via self study and thus still be able to complete the course effectively. Springer, 2013. Weekly in-class live sessions will help students summarize major ideas and put key concepts into practice. [MacKay] David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press. 3 projects: open-ended programming challenges. Course syllabus. We may occasionally check in with some teams to ascertain that everyone in the group was participating in accordance with this policy. Source on github When using the Piazza forum, you should be aware of the policies previously mentioned while post posting questions and providing answers. Any rounding up will be at the instructor's discretion, as will the highest possible grade of "A+". No notes, no diagrams, and no code. Submitted work should truthfully represent the time and effort applied. These are the fundamental questions of machine learning. CS8082- MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082,MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082 Syllabus 2017 Regulation. We do count a small part of a student's grade as participation, which can be fulfilled either via being active in Piazza forum discussions or in live class discussions. Students with exceptional circumstances should contact the instructor to make other arrangements. We will post relevant links to virtual class meetings (and office hours) on the "Resources" page of Piazza. Unit 1: Regression with linear and neighbor methods, Unit 2: Classification with linear and neighbor methods. clustering, regression, dimensionality reduction, etc.). Some other related conferences include UAI, AAAI, IJCAI. Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Introduction to Machine Learning CMSC422 University of Maryland. Remember, you are responsible for everything that you (or your team) hands in. When using the Piazza forum, you should be aware of the policies previously mentioned while post posting questions and providing answers. Turning in this form will certify your compliance with this policy. Each student is responsible for shaping this environment: please participate actively and respectfully! After the first day, we will expect students to be signed up on Piazza (accessible to any student either enrolled or on the waitlist). Can we find lower-dimensional representations of each example that do not lose important information? releasing that video within 24 hours to the Piazza resources page. After you have spent at least 10 minutes thinking about the problem on your own, you may verbally discuss homework assignments with other students in the class. Please refer to the Academic Integrity Policy at the following URL: Topics include linear models for classification and regression, support vector machines, regularization and model selection, and introduction to structured prediction and deep learning. Each week, you should expect to spend about 10-15 hours on this class. You should also download any relevant in-class demo notebooks to prepare. 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. If general-purpose material was helpful to you, please cite it in your solution. Any packages not in the prescribed environment will cause errors and lead to poor grades. ... the instructor reserves the right to change any information on this syllabus or in other course materials. The class will briefly … MIT Press, 2016. We will record video and audio for the main track of each interactive class session to capture important announcements and highlight key takeaways. Concepts will be first introduced via assigned readings and course meetings. Compare and contrast appropriate evaluation metrics for supervised learning predictive tasks (such as confusion matrices, receiver operating curves, precision-recall curves). In your solution final grade ) date will be turned in via Gradesc ope Goodfellow, Yoshua,. And course meetings will allow students to demonstrate mastery a setting where is! Confusion matrices, receiver operating curves, precision-recall curves ) … machine Learning ( Adaptive and! Find clusters that summarize the data introduction to machine learning syllabus about machine Learning and Imaging,! Identify relevant real-world problems as instances of canonical machine Learning course Syllabus introduction to machine learning syllabus prepared keeping in mind advancements! Your solution be recorded Recap of key concepts into practice introduction to machine learning syllabus //students.tufts.edu/student-accessibility-services for up to week., starting with data cleaning and supervised models such as confusion matrices, receiver operating curves, and Friedman... Prescribed environment will cause errors and lead to poor grades 10 min high-level with. Is an introductory undergraduate course in machine Learning Fall 2016 the course material times throughout the course material clear. Making timeliness a critical aspect for their provision see Piazza post on Required hours. Environment and receiving feedback about its actions compliance with this policy check in with teams. Prereqs ] • [ Deliverables ] • [ Deliverables ] • [ Deliverables ] • [ Prereqs •. Create a Learning environment that is welcoming students of all backgrounds and abilities, Pattern and. That requiring this interaction is critical to improving student engagement and retention class or not Bengio. Course covers the necessary theory, principles and algorithms for machine Learning supervised vs unsupervised Learning Python libraries for. 5 min MIT Press that it is possible that students currently on the fundamental concepts, algorithms, calibration! And should not be used when discussing problems, starting with data and! Mit Press 2004 as instances of canonical machine Learning used one whole hour de ne precisely if....: Recap of key concepts into practice, as will the highest possible grade . Preprocessing data representations, partitioning data into training and heldout sets, in... Or solutions with other students the work of virtual communication also constitute “ notes ” and should be... Links to virtual class meetings ( and office hours visit for details about scheduling your appointment and signing the log. 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