MC886 / MO444 - Machine Learning and Pattern Recognition
Computing Institute (IC / Unicamp)Prof. Sandra Avila (sandra@ic.unicamp.br)
Time and Place: Tuesdays and Fridays, from 19pm to 21pm. Living room PB18.
The course will start on 08/08/2017, due to Unicamp's VII Computing Week (SECOMP), from 31/07/2017 to 04/08/2017.
Waiters The opening hours will always be provided after classes by the teacher, or on Mondays from 16 pm to 18 pm in the teacher's room, or scheduled in advance by email with PED Samuel Fadel, samuel.fadel@ic.unicamp.br .
Course Program: Introduction to Machine Learning, Linear Regression, Logistic Regression, PCA and LDA, K-means, Neural Network, Deep Learning, SVM and Kernels, Boosting and Random Forest.
Programming language: The programming language used in the course is free, as long as it is compatible and justified in the context of the problem. Recommendations: Python, R, Matlab.
Assessment: The evaluation will be based on active classroom participation, proposed activities, and practical projects, being:
- At the end of the class, the student must send via Moodle what was the point of biggest doubt of the class (MD), a succinct question. If you have no doubts (seriously?), The student should highlight the point that he found most interesting. Answers like “I had no doubts” or blank answers will not be accepted.
- Three double tasks, T1, T2 and T3. The code and the report must be delivered via Moodle.
- Um final project PF to be carried out in groups:
- The student will only be able to do the final PF project if he has delivered the three tasks T1, T2 and T3.
- Groups must have 4 to 5 students, necessarily.
- The code and the report must be delivered via Moodle, and the report must present an explanation of the technique implemented, illustrations of the results, and a discussion of the results obtained in the format of a scientific article, in the model suggested by the teacher.
- The project must be presented in class by the group on the scheduled date.
- The final average, M, will be calculated as: M = 0,05 x MD + 0,2 x T1 + 0,2 x T2 + 0,2 x T3 + 0,35 x PF
- For undergraduate students, the following rule will apply:
- Approved: if M ≥ 5.0
- Failed: if M <5.0
- For graduate students, the final grade will be assigned as follows:
- A: if M ≥ 8.5
- B: if 7.0 ≤ M <8.5
- C: if 5.0 ≤ M <7.0
- D: if M <5.0
- For undergraduate students, the following rule will apply:
Submission of Activities: All course activities must be submitted through the system Moodle in the corresponding area of ​​the discipline.
Evaluation Delivery Dates: The dates below are subject to change.
- Biggest Doubt (MD): Until 12:XNUMX the next day after class.
- Task 1 (T1): 01/09/2017
- Task 2 (T2): 10/10/2017
- Task 3 (T3): 10/11/2017
- Final Project (PF):
- Proposal submission: 13/10/2017
- PF submission (code and report): 08/12/2017
- Presentation (videos up to 4 minutes): 01 / 12 / 2017
Remarks:
- There will be no tests or exam for this discipline.
- Any attempted fraud in the activities of the discipline will imply a final average M = 0 (zero) for all persons involved, without prejudice to other sanctions.
References: The teacher will not follow a specific textbook, however, the following books cover what will be seen in class:
- “Machine Learning: A Probabilistic Perspective,” Kevin P. Murphy, 2012.
- “Pattern Recognition and Machine Learning”, Christopher M. Bishop, 2006.
- "Pattern Classification", David G. Stork, Peter E. Hart, and Richard O. Duda, 2000.
- “Deep Learning”, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016.
- “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Aurélien Géron, 2017.