CSE 5523: Machine Learning

Introduction to basic concepts of machine learning and statistical pattern recognition; techniques for classification, clustering and data representation and their theoretical analysis.

Course learning outcomes: Upon successful completion of this course, each student will be able to:

  1. Understand basic concepts of machine learning and statistical pattern recognition
  2. Implement basic machine learning and pattern recognition algorithms
  3. Know how to compare, interpret and analyze results from the machine learning and pattern recognition methods
  4. Apply classical machine learning and pattern recognition techniques for data analysis
  5. Have basic ideas on how to develop proper machine learning and pattern recognition methods for different problems
  6. Be able to implement a data analysis pipeline for a real-life problems using the concepts and techniques learned from this course

Instructor: William Schuler

TA: Mike Menart

Meeting time and location: Tuesday and Thursday 9:35am-10:55am in Caldwell Lab 120.

Web site: http://www.ling.osu.edu/~schuler/courses/5523. The updated syllabus, assignments, slides, etc. will be posted here, so check it regularly.

Textbook: (optional) Kevin Murphy Machine Learning: a Probabilistic Perspective ISBN: 9780262018029

Course Content:

Wk Due Monday 11:59PM Lecture: Tuesday Due Wednesday 11:59PM Lecture: Thursday
1 8/24 python, pandas tutorials
overview, background
Murphy ch 2.1-2.5 8/26
probability, distribution functions
2 (no class) Murphy ch 3 9/2 --- PS1 handout
generative models
3 Murphy ch 5.1-5.4 9/7
Bayesian statistics
Murphy ch 5.7, ch 6-6.2.1 9/9
decision theory, significance, permutation testing
4 9/13 PS1 due,
Murphy ch 2.8
9/14 --- PS2 handout investor-train.csv investor-test.csv
information theory
Murphy ch 16.1-16.4 9/16
decision trees
5 9/21
linear algebra notation
Murphy ch 4.1 9/23
continuous variables (Gaussians)
6 9/27 PS2 due
Murphy ch 7.1-7.5
9/28 --- PS3 handout, sweet-train.csv, sweet-test.csv
linear regression
7 Murphy ch 12.2 10/5
dimensionality reduction, principal components
Murphy ch 8.1-8.3 10/7
logistic regression
8 10/11 PS3 due,
Murphy ch 8.5
10/12 --- PS4 handout, iris-train.csv, iris-test.csv
stochastic optimization, adam
(autumn break)
9 Murphy ch 16.5 10/19
multi-layer neural networks
convolutional neural nets
10 10/25 PS4 duei 10/26 --- PS5 handout, mileage-train.csv, mileage-test.csv
transformers, automatic differentiation
11 Murphy ch 14.1-14.2,14.4-14.5 11/2
support vector machines
Murphy ch 10-10.5 11/4
Bayes nets, HMMs, message-passing, inference
12 11/8 PS5 due,
Murphy ch 19.1-19.3,19.5-19.6
11/9 --- PS6 handout, games.csv
random fields
(Veterans Day)
13 Murphy ch 11.1-11.4 11/16
expectation maximization
Murphy ch 24.1-24.3 11/18
Dirichlet models, Gibbs sampling
14 11/22 PS6 due,
Murphy ch 25.1-25.2
Dirichlet process models
(Thanksgiving Day)
15 11/30
project presentations
project presentations
16 12/7
project presentations
(end of term)
17 12/13 project due (end of term) (end of term)

Credit hours and work expectations: This is a 3-credit-hour course. According to Ohio State policy, students should expect around 3 hours per week of time spent on direct instruction (instructor content and Carmen activities, for example) in addition to 6 hours of homework (reading and assignment preparation, for example) to receive a grade of (C) average.

Course requirements:

Student participation requirements: Consistent engagement is expected. If any problems arise relative to attendance, please contact the instructor as soon as possible. Communication is important. You are encouraged to participate during class, ask questions, work on in-class problems in small groups, and share your experiences relative to the subjects and discussion that day.

Attendance and active participation often impacts your performance in a meaningful way, so it will be beneficial for you to attend this course synchronously as much as possible. The lecture slides will be posted on CarmenCanvas, so if you do miss a lecture, you are expected to view the missed material before the next lecture.

Faculty feedback and response time:

Grading scale: OSU standard scheme
A A- B+ B B- C+ C C- D+ D
93%+ 90%+ 87%+ 83%+ 80%+ 77%+ 73%+ 70%+ 67%+ 60%+

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