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# System prepended metadata

title: What's Beyond CS181

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# What's Beyond CS181
### CS181
### Spring 2023
![](https://i.imgur.com/xDR9VQd.png)


## Classes in Machine Learning

**Theory:** Courses address theoretical aspects of machine learning, from statistical, information theoretic, mathematical, computational (etc) perspectives.
- **APMTH 226** *Theory of Neural Computation*
- **COMPSCI 128** *Convex Optimization and Applications in Machine Learning*
- **COMPSCI 229BR** *Topics in the Foundations of Machine Learning*
- **STAT 186** *Causal Inference*
- **APMTH 231** *Decision Theory*
- **ENG-SCI 250** *Information Theory*


**Survey Courses:** Courses that surveys different paradigms of machine learning models and inference algorithms.

- **APMTH (No Longer Offered)** [*Survey of Probabilistic Machine Learning*](https://onefishy.github.io/am207/)
- **MIT 6 .7900** *Machine Learning*
- **MIT 6 .7810** *Algorithms for Inference*
- **COMPSCI 282** *Topics in Machine Learning* (Topic Rotates Every Year)


**Natrual Language Processing:** Courses on natural language processing.
- **MIT 6 .8610** *Quantitative Methods for Natural Language Processing*

**Explanability, Socio-Technical Systems:** Courses on explanable AI, HCI and socio-technical systems.
- **COMPSCI 282BR** *Topics in Machine Learning: Interpretability and Explainability*
- **COMPSCI 276** *Design, Technology, and Social Impact*

**Reinforcement Learning:** Courses on reinforcement learning.
- **COMPSCI 184** *Introduction to Reinforcement Learning*
- **Stat 234** *Sequential Decision Making*


**Bayesian Machine Learning:** Courses on Bayesian modeling.
- **STAT 120** *Introduction to Bayesian Inference and Applications*
- **STAT 220** *Bayesian Data Analysis*
- **MIT 6 .7830** *Bayesian Modeling and Inference*

**Unsupervised Learning:** Courses on unsupervised learning.
- **STAT 185** *Introduction to Unsupervised Learning*


**Machine Learning in Context:** Machine learning as an interdisiplinary study.
- **MIT 6 .7930** *Machine Learning for Healthcare*
- **DPI 617** *Law, Order and Algorithms*
- **HLS 3040** *Algorithms, Rights, and Responsibilities*
- **APCOMP 298R** *Interdisciplinary Seminar in Applied Computation: Diversity, Inclusiona and Leadership in Tech*
- **APCOMP 221** *Critical Thinking in Data Science*
- **COMPSCI 288** *AI for Social Impact*
- **COMPSCI 197** *AI Research Experiences*




## Doing Research in Machine Learning
As Harvard students you have many opportunities to participate in research, in particular, machine learning research.

### Why You Might Want to Do Research

You might want to do research for many different reasons, there is no right reason to do research:
- You are considering a PhD and want to know if you like research
- You like working on open-ended problems
- You like working with a team of friends on a challenge
- You want to solve a real-world problem
- You want to present your results in an ML workshop so you can meet other researchers in ML and connect with the wider ML community


### What Research is Not
Common misconceptions about research:
- Research is done by a lone genius (usually by deriving lots of math)
- Research is done by making monumental breakthroughs from epiphanies
- The value of research is in the eyes of reviewers of prestigious conferences and journals
- Research skills are inborn; some people are just better at it 
- Research is always fun and driven by burning intellectual fire and moral passion


### What Is Research
In reality, research is much more mundane and accessible/rewarding.
- Research is almost alway done in a team of collaborators who bring different skills, interests and perspectives (and personalities)
- Most questions can be tackled fruitfully in many different ways (not just by doing lots of fancy math)
- Research is done by iterating the process of asking very concrete, narrowly-scoped questions each week, designing experiments to answer these questions, then generating new questions 
- The value of research is what it means to you (you need to find an independent reason for doing research)
- No one is born with research skills and everyone has to work hard to gain them
Sometimes research can be exciting/fun, but it can also be very frustrating (you need to find a sustainable reason for doing research)

### What You'll Get Out of Doing Research
Most people think the point fo doing research is to make breakthroughs and shift human understanding of the world. In reality, it takes many many years (even as a PhD student) to be able synthesize enough existing work to make headway on an impactful problem. 

Instead, the main point of doing research as an undergraduate student or a junior grad students is to *learn the fundamental skills necessary for doing research*. 

That is, the point of doing research at this state in your career is to ***learn how to do research***. 