The repository is under constant development. Check https://github.com/NeuromatchAcademy/course-content for recent updates.
- Python Tutorials
- Model Types
- Modeling Practice
- Model Fitting
- Machine Learning
- Dimensionality Reduction
- Bayesian Statistics
- Linear Systems
- Decision Making
- Optimal Control
- Reinforcement Learning
- Real Neurons
- Dynamic Networks
- Network Causality
- Deep Learning
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Going unprepared could spell disaster:
The students are counting on your experience so, look through the tutorials beforehand.
If you really don't have time on a particular day, at least glance through the tutorials so, you're not caught by surprise. -
Be kind to yourself:
With content so diverse, it's impossible to be an expert on everything.
Accept that you don't know, tell your students that you will get back to them and keep your word. -
Take the precourse seriously:
Look up the precourse content as they might help further in making neural correlates when necessary during the course.
Or least, help you "get in the zone". -
Form an internal support system:
Your pod relies on you and at times, its' okay for you to rely on them too. -
Take your creative self to work:
Try to form unusual connections between concepts and get the students to understand why they're doing what they're doing.
And to be creative, sometimes All you need to do is… nothing. -
Know thyself:
Understand where your weaknesses lie - is it your interpersonal skills, technical understanding, communicating ideas? Teaching has a lot to do with your personality, and fixing the potholes will pay in the longer run! -
Be attentive:
Watch the non-verbal atmosphere of the day.
If the students are bored/exhausted, try to brighten the day by engaging with them.
One secret of leadership is that the mind of a leader never turns off. Leaders even they are sightseers or spectators, are active; not passive observers. ~ James Humes
- Take care:
The most important thing you can do is take care of yourself. If you're turn up tired, there's no way your students can be excited!
Special Note: Introvert TA's! Being around people all day is no easy feat. Take time away from screens and curl with music or books every day after hours. Communicate with your students if and when necessary!
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Try to be as proactive in your learning as possible:
Skim through the next day's tutorials, and look into the additional resources of the current day's tutorial.
Try to understand how this connects to your research, to your formal coursework or
simply try to understand potential applications.
You could also look up related, latest research papers. If you're clueless, request your TA to suggest resources. -
Don't be afraid to volunteer : Who wants to share their colab notebook? ME! Who wants to share their screen? ME!
Volunteer for this is a great opportunity to share what you learn and show your skills within the pod.
If you're not confident, pick topics that you are confident with, or go through the tutorials in advance. But do it at twice! -
Talk to your friends in other pods : Look for ways to reach out to other students. Join groups on telegram, chat or slack and use all resources available to you.
Ask them about their day, what interesting incidents happened in their pod on that day.
You'll be surprised by how much you can learn. Take these learnings back to your pod and to your TA.
The real take away is the friends you make along the way!!
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Anything you're going through, talk to your TA : If there's anything that is on your mind or something within the pod that is bothering you, TA is your guy/gal.
Remember, they're here for you and want the best for you! -
Handle Zoom fatigue:
Sitting in front of your system all day with intensive tutorials can be exhausting, so be kind to yourself.
Surround your self with fresh juices, coffee or anything you'd find comfort in. Ask your TA for breaks to walk around and sustain your focus. -
Use your weekends well: Use your weekends to possibly step out or spend some me time, attend the keynotes/additional classes from the calendar or look up next week's tutorials.
They can help you refresh, and recharge for your week ahead.
It is recommended that you use this list to look up topics you are not particularly comfortable with. For the entire list of videos and other content, refer here. Side Note: The list below can get overwhelming. Choose wisely!
- Reinforcement Learning: https://cs182sp21.github.io/ http://rail.eecs.berkeley.edu/deeprlcourse/