Data science methods 1 syllabus (fall 2020)

Course instructor

Dr. Venkat N. Gudivada

⊕
Dr. Venkat N. Gudivada

⊕ http://www.cs.ecu.edu/gudivada/, Email: gudivadav15@ecu.edu, Phone: (252) 328 - 9680, Office: SciTech Room 106

Office hours and course communications

TR, 3:00 PM - 5:00 PM and W 8:00 PM - 10:00 PM. All office hours will be held on Microsoft Teams. All communications about the course will occur on Microsoft Teams. Please install Microsoft Teams on your personal computer (it is free for ECU students).

Course description

This is a Fall 2020 cross-listed course of the following: DASC 6000-001 and DASC 6000-601.

This is a 15-week, full-term course. The course will be offered in hybrid face-to-face (HF2F) mode for students enrolled in DASC 6000-001. The course will meet on MW, 17:00 PM - 18:15 PM. Hybrid delivery means that the course will employ both in-person and online classes. Details will be forthcoming.

For students enrolled in DASC 6000-601, the course will be delivered 100% online, asynchronously.

Course teaching assistant

• James T. Vincent, email: vincentja19@students.ecu.edu

Course website

Data Science Methods I (Probabilistic perspective on machine learning)

Recommended books

1. Bertsekas, D. P., & Tsitsiklis, J. N. (2008). Introduction to Probability (2nd ed.). Athena Scientific. http://athenasc.com/probbook.html
2. Pishro-Nik, H. (2014). Introduction to Probability, Statistics, and Random Processes. Kappa Research. https://www.probabilitycourse.com/
3. Lewis, H., & Zax, R. (2019). Essential Discrete Mathematics for Computer Science. Princeton University Press.

Reference books

1. Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
2. Bishop, C. (2007). Pattern Recognition and Machine Learning. Springer.
3. Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.
4. Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for Machine Learning. Cambridge University Press. https://mml-book.github.io/
5. Faul, A. C. (2019). A Concise Introduction to Machine Learning. Chapman and Hall/CRC.
6. Hastie, T., Tibshirani, R., & Friedman, J. (2013). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
7. Mitzenmacher, M., & Upfal, E. (2017). Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis (Second ed.). Cambridge University Press.
8. Morin, D. J. (2016). Probability: For the Enthusiastic Beginner. CreateSpace Independent Publishing Platform.
9. Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. The MIT Press.
10. Pfeffer, A. (2016). Practical Probabilistic Programming. Manning Publications.

Student learning outcomes

After successful completion of the course, the students will be able to do the following:

1. Apply knowledge of set theory, permutations and combinations, and sums to solving probability problems.

2. Apply knowledge of functions, relations, recurrences, and elementary graph theory to solving data science problems.

3. Apply knowledge of linear algebra to solving information retrieval problems.

4. Apply conditional and joint probability, discrete and continuous random variables, and expectation concepts to solving data science problems.

5. Model solutions to data science problems using Markov and Poisson processes and validate solutions.

6. Apply Poisson process for counting the occurrences of random events that appear to happen at a certain rate.

Major course topics

• Review of set theory, combinatorics, sums, functions, relations, recurrences, essence of differentiation and integration, elementary graph theory, and essential linear algebra.
• Probabilistic models
• Discrete random variables
• General random variables
• Inequalities and limit theorems
• Random processes and Markov chains
• Bayesian statistical inference
• Classical statistical inference

Respect for Diversity

It is my intent to serve well in this course all students from diverse backgrounds and perspectives. Students’ learning needs will be addressed both in and out of class. The diversity that students bring to this class be viewed as a resource, strength and benefit. I will strive to present the course content and learning activities that are respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. Your suggestions are encouraged and appreciated. Please let me know ways to improve the effectiveness of the course for you personally or for other student groups.

Course assessment and grading scale

• Written assignments (20%)
• Programming assignments (25%)
• Midterm exam (20%)
• Final exam (30%)
• Innovative contribution to online course content (5%)

Extra credit (up to 5%) assignments are available. Those who wish to seek extra credit, check with the course instructor.

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