CSE 390HA, 142 Honors, Autumn 2017

Week #11 (12/6) - Victory Lap

In our last section this week we recapped what we have learned in the honors section and looked at some next steps for things to learn on your own.

Week #10 (11/29) - Industry Panel

This week we will hear the experiences of some awesome people who have worked in industry and teaching. We heard their experiences about working at big companies and their strategies for getting their foot in the door to tech. Our panelists:

• Erika Wolfe - CSE undergrad/grad student. Erika is currently a 143 TA and has interned for Microsoft, Square, and Facebook
• John Kaltenbach - CSE grad student. John is currently a TA for the senior level machine learning class. John has interned at Indeed, Facebook, and Sift Science.
• Cherie Ruan - CSE/Informatics undergrad. Cherie is currently a 143 TA. Cherie has interned for Boeing and will be interning for Facebook this summer.
• Whitaker Brand - Your lecturer for 143 and also is a developer on our software tools (PracticeIt and GradeIt). Whitaker has interned and worked full time for Amazon.

Week #9 (11/22) - No section

Have a good thanksgiving!

Week #8 (11/15) - Data Visualization

This week we talked about how data visualization can influence your opinion, how humans interpret data and visuals (like color, position accuracy, etc), and the effects that has on the design of data visualizations. We talked about what makes data visualizations good and bad and how we could maybe deceive people using data. Below is a list of things I showed off in class:

• Slides
• Data visualizations shared by students
  • Income and Life Expectancy
  • Number of People by Income
  • Electoral College
  • Gun Deaths - Years Lost
  • Gun Deaths - Multifaceted
  • SelfieCity
  • All submissions
• Data visualizations I like
  • Wind Map
  • Boston Subway
  • Zip Codes
  • Dear Data
  • Change in Times (made by UW students Halden Lin, Shobhit Hathi, Lilian Liang, Gunnar Olson)
  • Police Killings in US (made by UW students Hunter Schafer, Erika Wolfe, Nick Mooney, Meredith Lampe)
  • Other Data visualization projects
• Tools
  • d3.js
  • Chart.js (recommended over d3 for starting
  • Data Voyager
  • Idyll for data stories

Homework for next session:

• No section week of Thanksgiving so nothing to do this week!
• For the following week, think of some questions for people in the CSE industry / academic field. We'll spend the time figuring out what you want to know. Some examples inlcude:
  • Did you do any research, how did you get into that, what was it like?
  • Did you do any internships, how did you get hired, what was it like?
  • What is it like to do coding at a production scale?
  • What classes have you enjoyed the most at UW?
  • What skills besides cs do you find most useful in your career?

Week #7 (11/8) - Web Programming Introduction

This week we showed off how to make your own website and the languages you need to know to do that (HTML, CSS, Javascript). I demonstrated how to use those languages to make a simple website as a starting point for if you want to make your own website.

• first_page.html
• puppies.html
• puppy-stlye.css
• puppy-script.js
• CSS Zen Garden

Homework for next session:

• Watch a short video on the future of data vis from Jeff Heer, Professor at UW.
• Watch a short video on Hans Rosling demonstrating how data is important for global planning and reasoning about the current state of the world.
• Find a cool data visualization that conveys some interesting information about the world. If you're stuck, politics are usually an interesting place to start.
• Fill out reading response here
• (Optional) I would love to get some feedback on what you think about our section. Please fill out this anonymous form if you are interested in providing feedback.

Week #6 (11/1) - No Meeting

Homework for next session:

None

Week #5 (10/25) - Public Key Cryptography and Security

In this section, we discussed the mechanism for public key cryptography and how modern cryptography works. We talked about some of the high level details of how the algorithms worked and the history of their use. We also talked about how these algorithms rely on the difficulty of certain problems and how this relates to the famous P vs NP problem.

Interesting Links

• 99% Invisible - Vox Ex Machina

Homework for the next session:

None! You have a midterm next Friday so there is no homework. We will be going over web development (HTML, CSS, Javascript) in class next week. If you want you can look over tutorials for these concepts but I will assume you have no knowledge of it when coming next week

Week #4 (10/18) - Pattern Recognition, Machine Learning and Artificial Intelligence

In this section, we learned about 2 common machine learning solutions to classification and discussed some of the real world applications. We discussed some of the difficulties when doing machine learning and what to look out for. We introduced what it looks like to write machine learning code in a language called Python.

• Slides from Machine learning talk I gave
• Python code for using machine learning in python
• MNIST Handwriting Recognition Dataset

Homework for the next session:

• Read chapter 4, Public Key Cryptography, from 9 Algorithms That Changed The Future
• Watch this 20 minute video from UW professor, Yoshi Kohno, describing the internet of things and adversarial security
• Fill out this reading response form before Wednesday at 4:30

Week #3 (10/11) - How Google Works: Search Indices and PageRank

In this section, we discussed how basic indexing and the PageRank algorithm works. We talked about what matters for indexing including word location and other metadata. For PageRank we talked about the concept that what is important is for other important websites to link to you.

We also briefly covered how the internet works, what webpages are and how the look, and how Google would build up their search indices using web crawlwers

We discussed the implications that Google's decisions have on our society. This discussion ranged from what ads they show us, to autocomplete suggestions, and to the ranking of the search results themselves.

Homework for the next session:

Due at 4:30, right before class

• Read chapter 6, Pattern Recognition, from 9 Algorithms That Changed The Future. This chapter is about Machine Learning and applied statistics. We'll also talk next week about applications of these algorithms to Artificial Intelligence techniques.
• Watch video on Google DeepMind.
• Fill out reading response questionaire

Week #2 (10/4) - Introduction

In this section, I introduced the goals of the honors sections

• To explore the impact of technology and computer science concepts on society
• To explore computer science and its applications as an academic field
• To preview what a career as a professional computer programmer or researcher is like
• To look more indepth at how we apply computational thinking to our daily lives

We emphasized that 142 is about how to program computers while computer science (and computational/algorithmic thinking) is a much large scope than just programming. As a group, we discussed:

• What computer science even is
• What computers can and can't do (philosophy seems too novel for computers)
• A bit of the history of computer science and where it came from

Intersting Links

• What is computer science?
• The halting problem

Homework for next time:

• Fill out this survey with your experiences, interests, and goals for the course
• Read the introduction and first two chapters, Search Engine Indexing and Page Rank, from 9 Algorithms That Changed The Future

Week #1 (09/27) - No Meeting

Homwork

None!