class: center, middle, inverse, title-slide # STA 610L: Module 1.1 ## Course overview ### Dr. Olanrewaju Michael Akande --- class: center, middle # Welcome! --- ## What is this course about? <i class="fa fa-book fa-2x"></i> Learning the foundations of hierarchical modeling. -- <i class="fa fa-folder-open fa-2x"></i> Working through the theory of some hierarchical models. -- <i class="fa fa-tasks fa-2x"></i> Using hierarchical models to answer inferential questions. -- <i class="fa fa-database fa-2x"></i> Applying hierarchical models to real datasets. -- <i class="fa fa-group fa-2x"></i> Honing collaborative and presentations skills. -- --- <i class="fa fa-quote-left fa-2x fa-pull-left fa-border" aria-hidden="true"></i> <i class="fa fa-quote-right fa-2x fa-pull-right fa-border" aria-hidden="true"></i> The Bayesian paradigm is well suited for building hierarchical models. Usually you just have several levels of conditional distributions making up the prior. --- ## Instructor [Dr. Olanrewaju Michael Akande](https://olanrewajuakande.com) <i class="fa fa-envelope"></i> [email@example.com](mailto:firstname.lastname@example.org) <br> <i class="fa fa-home"></i> [https://sta610-f21.olanrewajuakande.com](https://sta610-f21.olanrewajuakande.com) <br> <i class="fa fa-calendar"></i> Mondays and Thursdays (9am -- 10am) <br> <i class="fa fa-university"></i> Zoom Meeting ID: **See Sakai** --- ## TAs [Chengxin Yang](https://scholars.duke.edu/person/chengxin.yang) <i class="fa fa-envelope"></i> [email@example.com](mailto:firstname.lastname@example.org) <br> <i class="fa fa-calendar"></i> Tuesdays (1pm - 2pm) and Fridays (4pm - 5pm) <br> <i class="fa fa-university"></i> Zoom Meeting ID: **See Sakai** [Alessandro Zito](https://scholars.duke.edu/person/alessandro.zito) <i class="fa fa-envelope"></i> [email@example.com](mailto:firstname.lastname@example.org) <br> <i class="fa fa-calendar"></i> Wednesdays (10am - 11am) and Fridays (8:30am - 9:30am) <br> <i class="fa fa-university"></i> Zoom Meeting ID: **See Sakai** --- ## FAQs All materials and information will be posted on the course website. -- - How much theory will this class cover? *A decent amount.* -- - Am I prepared to take this course? *Yes, if you are familiar with the topics covered in STA 360/601/602 (Bayesian Inference) and all its prerequisite at Duke.* -- - What if I can't remember the topics in the prerequisites? *See the review materials in the next module.* -- - Will we be doing "very heavy" computing? *Not too heavy but a good amount.* -- - What computing language will we use? *R!* -- - What if I don't know R? *This course assumes you already know R but you can still learn on the fly (you must be self-motivated). Here are some resources for you: [https://sta610-f21.olanrewajuakande.com/resources/](https://sta610-f21.olanrewajuakande.com/resources/).* --- class: center, middle # Course structure and policies --- ## Course structure and policies - See: [https://sta610-f21.olanrewajuakande.com/policies/](https://sta610-f21.olanrewajuakande.com/policies/) -- - Make use of the teaching team's office hours, we're here to help! -- - Do not hesitate to show up during office hours. You can also make an appointment to discuss a homework problem or any aspect of the course. -- - When the teaching team has announcements for you we will send an email to your Duke email address. Please make sure to check your email daily. -- - Try as much as possible to refrain from texting or using your computer for anything other than coursework while in class. --- ## Other details - What topics will we cover? Refer to Section 11 of the syllabus (here: [Syllabus](https://sta610-f21.olanrewajuakande.com/syllabus_pdf/Syllabus.pdf)). -- - Also, refer to the schedule on the website for an updated breakdown of the topics. Remember to refresh the page frequently. See here: [Class Schedule](https://sta610-f21.olanrewajuakande.com/syllabus/). -- - If you are auditing this course, remember to complete the necessary audit forms. -- - Confirm that you have access to Sakai, Ed Discussion and Gradescope. -- - Finally, please respect Duke's masking and social distancing policies, both in and out of classes. --- class: center, middle # What's next? ### Move on to the readings for the next module!