シラバス参照
シラバス参照
| 授業科目名 /Course title (Japanese) |
応用ネットワーキング論２ | ||
|---|---|---|---|
| 英文授業科目名 /Course title (English) |
Ｎｅｔｗｏｒｋ Ａｐｐｌｉｃａｔｉｏｎｓ ２ | ||
| 開講年度 /Academic year |
2019年度 | 開講年次 /Year offered |
全年次 |
| 開講学期 /Semester(s) offered |
後学期 | 開講コース・課程 /Faculty offering the course |
博士前期課程、博士後期課程 |
| 授業の方法 /Teaching method |
講義 | 単位数 /Credits |
2 |
| 曜限 /Day, Period |
木／Thu 2 | ||
| 科目区分 /Category |
選択科目 | ||
| 開講類・専攻 /Cluster/Department |
情報ネットワークシステム学専攻 | ||
| 担当教員名 /Lecturer(s) |
笠井 裕之 | ||
| 居室 /Office |
東2-611 | ||
| 公開E-mail |
笠井<hiroyuki.kasai@waseda.jp> | ||
| 授業関連Webページ /Course website |
http://www.kasailab.com/lecture | ||
| 更新日 /Last update |
2019/10/07 07:38:02 | 更新状況 /Update status |
公開中 /now open to public |
| 対面授業・遠隔授業の別 /Face-to-face or online lecture |
対面授業 |
|---|---|
| その他 /Others |
- Students who are interested in machine learning, pattern recognition, and big data analysis are welcome. - It is recommended to contact the lecturer by e-mail if you have any questions. - The spoken language can be English if less-than one non-Japanese student attends. - Black board is used. - Matlab simulation tasks are provided to students for their deeper understandings. |
| キーワード /Keywords |
Optimization problem, Non-linear programming, Gradient, Hessian, Convex set/function, optimality conditions, Iterative gradient descent fundamentals, Line search methods (Back-tracking, Armijo condition，Wolfe condition), Steepest descent, Newton's method, Quasi Newton's method, Conjugate gradient, Scaled/Preconditioning descent methods, Stochastic gradient descent |