日本語 English
| 開講年度/ Academic YearAcademic Year |
20232023 |
| 科目設置学部/ CollegeCollege |
人工知能科学研究科/Graduate School of Artificial Intelligence and ScienceGraduate School of Artificial Intelligence and Science |
| 科目コード等/ Course CodeCourse Code |
VR116/VR116VR116 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
|
| 授業形態/ Class FormatClass Format |
オンライン（全回オンライン）/Online (all classes are online)Online (all classes are online) |
| 授業形態（補足事項）/ Class Format (Supplementary Items)Class Format (Supplementary Items) |
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| 授業形式/ Class StyleCampus |
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| 校地/ CampusCampus |
他/OtherOther |
| 学期/ SemesterSemester |
春学期/Spring SemesterSpring Semester |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
土3･X308/Sat.3･X308 Sat.3･X308, 土4･X308/Sat.4･X308, Sat.4･X308, 土3･X309/Sat.3･X309, Sat.3･X309, 土4･X309/Sat.4･X309, Sat.4･X309 |
| 単位/ CreditCredit |
22 |
| 科目ナンバリング/ Course NumberCourse Number |
AIR5610 |
| 使用言語/ LanguageLanguage |
日本語/JapaneseJapanese |
| 履修登録方法/ Class Registration MethodClass Registration Method |
科目コード登録/Course Code RegistrationCourse Code Registration |
| 配当年次/ Grade (Year) RequiredGrade (Year) Required |
配当年次は開講学部のR Guideに掲載している科目表で確認してください。配当年次は開講学部のR Guideに掲載している科目表で確認してください。 |
| 先修規定/ prerequisite regulationsprerequisite regulations |
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| 他学部履修可否/ Acceptance of Other CollegesAcceptance of Other Colleges |
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| 履修中止可否/ course cancellationcourse cancellation |
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| オンライン授業60単位制限対象科目/ Online Classes Subject to 60-Credit Upper LimitOnline Classes Subject to 60-Credit Upper Limit |
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| 学位授与方針との関連/ Relationship with Degree PolicyRelationship with Degree Policy |
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| 備考/ NotesNotes |
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| テキスト用コード/ Text CodeText Code |
VR116 |
The purpose of this course is as follows:
(1) to develop the fundamental skills required to perform data analysis
(2) to foster advanced skills in implementing machine learning methods taught in classroom sessions of the Machine Learning course
(3) to evolve practical abilities to utilize machine learning via various methods relevant to data analysis.
This course includes introductory, primary, and supplementary topics. Introductory awareness involves hands-on exercises conducted to support entry level students. Primary subject matter concerns the implementation and evaluation of the methods explained in the lectures delivered in the "Machine Learning" course. Supplementary material refers to hands-on practices relevant to the utilization of machine learning. The first few lectures will be devoted to introductory awareness. Subsequent lectures will alternately attend to primary and supplementary topics.
※Please refer to Japanese Page for details including evaluations, textbooks and others.
この授業では、(1) 機械学習の実践に必要な基本的な技能を身に着け、さらに、(2) 講義「機械学習」で学習した機械学習の手法の実装、および、(3) 機械学習を使ったデータ分析に関連する様々な手法の利用方法を習得する。
The purpose of this course is as follows:
(1) to develop the fundamental skills required to perform data analysis
(2) to foster advanced skills in implementing machine learning methods taught in classroom sessions of the Machine Learning course
(3) to evolve practical abilities to utilize machine learning via various methods relevant to data analysis.
この授業は入門トピック、主トピック、そして副トピックから成る。入門トピックは、計算機によるデータ処理に慣れていない学生向けのハンズ・オン実習、主トピックは、科目「機械学習」で説明された手法の実装と評価、副トピックは、機械学習の利用に関連するその他の手法の実習である。最初の数回は入門トピックに、残りは交互に主トピックと副トピックに充てられる。
This course includes introductory, primary, and supplementary topics. Introductory awareness involves hands-on exercises conducted to support entry level students. Primary subject matter concerns the implementation and evaluation of the methods explained in the lectures delivered in the "Machine Learning" course. Supplementary material refers to hands-on practices relevant to the utilization of machine learning. The first few lectures will be devoted to introductory awareness. Subsequent lectures will alternately attend to primary and supplementary topics.
| 1 | Introductory topic 1 |
| 2 | Introductory topic 2 |
| 3 | Introductory topic 3 |
| 4 | Primary topic 1 |
| 5 | Supplementary topic 1 |
| 6 | Primary topic 2 |
| 7 | Supplementary topic 2 |
| 8 | Primary topic 3 |
| 9 | Supplementary topic 3 |
| 10 | Primary topic 4 |
| 11 | Supplementary topic 4 |
| 12 | Primary topic 5 |
| 13 | Supplementary topic 5 |
| 14 | Primary topic 6 |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
座学の授業「機械学習」での対応する内容をよく復習してからこの授業に臨むこと。
授業中に出した課題は締切日までに提出すること。
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
授業への参加度と複数回のレポート(100%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| 授業中に適宜指示する。 |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| 授業中に適宜指示する。 |