Stats 216 Stanford Homework !!BETTER!!

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STATS 216: Introduction to Statistical LearningOverview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered via video segments (MOOC style), and in-class problem solving sessions. Prereqs: Introductory courses in statistics or probability (e.g., Stats 60), linear algebra (e.g., Math 51), and computer programming (e.g., CS 105). Elective.Terms: Win | Units: 3 | Grading: Letter or Credit/No CreditInstructors: Tibshirani, R. (PI)

Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your mystanfordconnection account at any time.

Problem sets posted on the class website will be due in class onThursdays at the start of lecture. If you are traveling, you may email your solution to one of the course staff in advance of the deadline. Ten percent of the homework value will be deducted for each day a homework is late. Exceptions will be made for documented emergencies. No credit will be given for homework submitted after solutions have been posted.

After attempting the problems on an individual basis, each student may discuss a homework assignment with up to two classmates.However, each student must write up his/her own solutions individually and explicitly name any collaborators at the top of the homework.

Some parents, in fact, have decided to opt out of the whole thing. The Washington Post reported in 2016 that some parents have just instructed their younger children not to do their homework assignments.

In 2013, research conducted at Stanford University found that students in high-achieving communities who spend too much time on homework experience more stress, physical health problems, a lack of balance in their lives, and alienation from society.

To conduct the study, researchers surveyed more than 4,300 students at 10 high-performing high schools in upper middle-class California communities. They also interviewed students about their views on homework.

The researchers also found that spending too much time on homework meant that students were not meeting their developmental needs or cultivating other critical life skills. Students were more likely to forgo activities, stop seeing friends or family, and not participate in hobbies.

Pope described one teacher she worked with who taught Advanced Placement biology, and experimented by dramatically cutting down homework assignments. First the teacher cut homework by a third, and then cut the assignments in half.

Course: News Analytics and Machine Learning (NYU FRE GY 7871 - I2)Term: Fall 2019, first halfInstructor: Andrew Arnold (aoa216@nyu.edu)Disclaimer: All views and opinions expressed by the instructor in this course are his own and do not reflect the views, opinions, or confidential information of any of his current or former employers.Office hours: by appointment GA/Grader/Tutor: TBDLocation: Rogers Hall, Room 216 (Brooklyn Campus)Time: Tuesdays, 6:00 PM - 8:41 PMCourse style: Given the small class size, the course will be taught as a colloquium. New topics will be introduced in interactive lecture format, and then discussed and expanded by the group. These topics will then be built upon in the team projects, which will be further discussed and presented to the class. Active class attendance and participation is required.Grading: Attendance and participation*: 25%Homework*: 10%Midterm exam: 15%Course project: 50% totalProject proposal: 15%Midterm presentation: 35%Final presentation: 50%* Note about late registration: Since the class only meets seven times and the first homework is assigned on the first day of class, it may be difficult to make up for missed homework and attendance if you miss even the first day of class. Please let me know if you are considering joining the class late so we can discuss the implications.

The fast-growing field of news analytics requires large databases, fast computation, and robust statistics. This course introduces the tools and techniques of analyzing news, how to quantify textual items based on, for example, positive or negative sentiment, relevance to each stock, and the amount of novelty in the content. Applications to trading strategies are discussed, including both absolute and relative return strategies, and risk management strategies. Students will be exposed to leading software in this space. Students will benefit from some familiarity with basic probability, statistics and programming (python), and an interest in natural language processing (NLP) or computational linguistics. While the course will introduce a few trading strategies, it will also focus on NLP as a tool in its own right, applicable to domains outside of quantitative trading strategies.There will be readings, discussion, homework, a midterm exam and a final project.Course outcomes:

To supplement the lectures, we will have a combination of reading and homework assignments. Reading assignments are short questions that can be answered by reading the slides and watching the videos. Homework assignments will be based primarily on Python and will require some code development and analysis.

The main pre-requisite is Python, which will be used for most of the homework assignments and for demonstrating the algorithms in class. We will use TensorFlow and Keras to learn about deep learning. See the Python setup guide for information on how to install Python.

DON'T contact me, as too many students then decide I am their personal consultant if I allow it. Anyway. I won't answer questions. PERIOD. I don't do your homework. I won't do consulting by mail. A retired mathematician, numerical analyst, woodworker, bridge player. 2b1af7f3a8