Course learning outcomes: Upon successful completion of this course, each student will be able to:
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 |
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2 | (no class) | Murphy ch 3 | 9/2 --- PS1 handout
generative models |
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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) |
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6 | 9/27 PS2 due Murphy ch 7.1-7.5 |
9/28 --- PS3 handout, sweet-train.csv, sweet-test.csv
linear regression |
9/30 (cont'd) |
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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 |
10/21 convolutional neural nets |
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10 | 10/25 PS4 duei | 10/26 --- PS5 handout, mileage-train.csv, mileage-test.csv
RNNs, LSTMs |
10/28 transformers, automatic differentiation |
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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 |
11/23 Dirichlet process models |
(Thanksgiving Day) | |
15 | 11/30 project presentations |
12/2 project presentations |
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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:
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%+ |
Students with Disabilities: The University strives to make all learning experiences as accessible as possible. If you anticipate or experience academic barriers based on your disability (including mental health, chronic or temporary medical conditions), please let me know immediately so that we can privately discuss options. To establish reasonable accommodations, I may request that you register with Student Life Disability Services. After registration, make arrangements with me as soon as possible to discuss your accommodations so that they may be implemented in a timely fashion. SLDS contact information: slds@osu.edu; 614-292-3307; slds.osu.edu; 098 Baker Hall, 113 W. 12th Avenue.
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