====== Seminars ======

There will be two main types of seminars within the course:
  * tutorials and exercises on the topics covered by lectures, algorithm implementation in Java,
  * consultations on assignments - individual analysis of the solved problem and discussions on the progress made and further steps towards completing the task.

There are also a midterm test and oral presentations of the assignment results planned on certain weeks.

Students use a development platform of their choice to implement the programs for their assignment (typically C/C++, Java, Matlab or Mathematica).

An expected average home preparation time is 5 hours per week.

^ Seminar ^ Date ^ Topic ^ Materials ^
| 1.        | 25.2.  | Survey of successful applications of neural networks | {{:courses:a4m33bia:ann_examples-2016.pdf|}} | 
| 2.        | 3.3.  | Data mining | [[http://www.ra.cs.uni-tuebingen.de/software/JavaNNS/welcome_e.html|JavaNNS]], {{:courses:ae4m33bia:javanns-win.zip|JavaNNS (Win)}}, {{:courses:a4m33bia:data.zip|}}, {{:courses:a4m33bia:javanns-mac.zip|JavaNNS (Mac)}}, {{:courses:a4m33bia:snnsv4.2.manual.pdf|}}, [[http://www.cs.bham.ac.uk/~jxb/NN/javaNNS/javaNNSguide.html|Quick Guide to javaNNS]]      [[http://rapidminer.com/ |RapidMiner]] [[https://my.rapidminer.com/nexus/account/index.html#downloads|RapidMiner download]] {{:courses:a4m33bia:glass.csv.zip|Glass data}} [[http://research.cs.tamu.edu/prism/lectures/iss/iss_l13.pdf|Three-way data splits]] |
| 3.        | 10.3.  | Successful applications of evolutionary algorithms, EA individual project assignment   | {{:courses:a4m33bia:applications_of_eas_2015.pdf|}}, {{:courses:a4m33bia:individual_projects_ea_2016.pdf|}}, {{:courses:a4m33bia:exercise03_ann_assignments-2016.pdf|individual projects ANNs}}|
| 4.        | 17.3.  | Evolutionary models (steady-state, generational, etc.) (Java) | {{:courses:a4m33bia:sga_cheatsheet_1.pdf|CheatSheet}}, {{:courses:a4m33bia:sga_source_codes.zip|}}, {{:courses:a4m33bia:sga_solution_source.zip|solution}} |
| 5.        | 24.3.  | Consultations on assignment, implementing MLP evaluation and back-propagation learning (Java) | {{:courses:a4m33bia:exercise02_mlp-bp.pdf|Cheatsheet}}, {{:courses:a4m33bia:mlp_cheatsheet2.pdf|Cheatsheet2}}, {{:courses:a4m33bia:mlp_bp.zip|Sources}}, {{:courses:a4m33bia:mlp_bp.zip|solution}} |
| 6.        | 31.3.  | Evolutionary algorithms: implementing and testing crossover/mutation operators (Java) | {{:courses:a4m33bia:tsp.zip|Sources}}, {{:courses:a4m33bia:cheatsheet_tsp.pdf|Cheatsheet}}, {{:courses:a4m33bia:ea_tsp_solution.zip|Solution}}, {{:courses:a4m33bia:tsp_references.txt|TSP references}} |
| 7.       | 7.4.  | Consultations on assignment  | | 
| 8.        | 14.4.  | Recurrent ANNs: synchronous/asynchronous evaluation (Java)  | {{:courses:a4m33bia:exercise04_rnn.pdf|Cheatsheet}}, {{:courses:a4m33bia:rnn.zip|Sources}} |
| 9.        | 21.4.  | Consultations on assignment | |
| 10.        | 28.4.  | **Midterm test**.  | {{:courses:ae4m33bia:sampletest.pdf|example test}}, [[courses:a4m33bia:test_results| Test results ]] | 
| 11.       | 5.5.  | Consultations on assignment  | | 
| 12.       | 12.5.  | multiobjective optimization: NSGA (Java)  | {{:courses:a4m33bia:moea.zip|Sources}}, {{:courses:a4m33bia:cheatsheet_nsga2.pdf|}} | 
| 13.       | 19.5.  | **Deadline for the program implementation**, presentations of the program implementations  | | 
| 14.       | 26.5.  | **Deadline for the report submission**, presentations of the program implementations, Credits  | | 

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