CSCI 1070 Introduction to Computer Science: Taming Big Data
This section of the course (Fall 2016) meets Monday, Wednesday, and Friday from 11:00AM to 11:50AM in Ritter Hall 115.
How does Netflix recommend movies that you'll like? How does your email system recognize which messages are spam? How does Facebook decide what stories to post on your news feed? How does Google translate automatically between dozens of languages? The answer in each case is the same: the computer "learns" how to solve these problems using large datasets of known solutions (for example, a large collection of emails labeled as spam or not-spam, or historical movie ratings of every Netflix user).
In this course we'll learn the basic techniques and algorithms in Machine Learning and see how they can be applied to these problems and others like them. We'll learn how to assemble real-world datasets using various web APIs and will learn how to apply machine learning algorithms using the Python programming language.
- What is Machine Learning?
- Python Crash Course
- Databases and Web APIs
- Decision Tree Classifiers
- Bayesian Classifiers
- k-Means Clustering
- k Nearest Neighbors
- Recommendation Engines
- Neural Networks
- Introduction to Natural Language Processing
- Exploring Social Graphs
- Ethics in Machine Learning
The textbook for the course is Data Science from Scratch by Joel Grus, O'Reilly Media Inc., 2015. The ISBN is 978-1-491-90142-7. You can get it directly from O'Reilly, from Amazon, or from the SLU bookstore. The code examples from the book are available from this forkable github repository.
For those of you who choose to use the lab computers, please read the department and university policies on appropriate use of computer systems.
Homework and Exams
I will give approximately eight in-class quizzes (roughly one every two weeks) throughout the semester; dates TBA, depending on our progress through the course material. The quizzes are usually true/false, multiple choice, and some short answer, and only take about 10-15 minutes at the beginning of class. I'll drop your lowest quiz score, but I will not allow you to make up quizzes that you miss because of absence or if you arrive late for class. Together the quizzes make up 50% of your final grade.
You will also be asked to do a semester software project related to some topic we cover in the course, accounting for 25% of your final grade. I'll give you some ideas as we approach the middle of the semester. Since we'll cover a lot of different things, this is a good opportunity for you to explore some particular topic in greater depth.
Finally, we'll have a machine learning "bakeoff" toward the end of the semester, where teams of students will work together to solve a fixed machine learning problem of my choosing. I'll provide you with labeled training data for you to learn from, and then we will evaluate your algorithm against a hidden test set. Prizes will be awarded to the winning team. The bakeoff will count for 25% of your final grade.
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Disability Services Academic Accommodations
Students with a documented disability who wish to request academic accommodations are encouraged to contact Disability Services to discuss accommodation requests and eligibility requirements. Please contact Disability Services, located within the Student Success Center, at <Disability_services@slu.edu> or 314-977-3484 to schedule an appointment. Confidentiality will be observed in all inquiries. Once approved, information about academic accommodations will be shared with course instructors via email from Disability Services and viewed within Banner via the instructor’s course roster.