diff --git a/courses/CSF320/README.md b/courses/CSF320/README.md index be2808a..939f81f 100644 --- a/courses/CSF320/README.md +++ b/courses/CSF320/README.md @@ -3,6 +3,7 @@ ## Overview Data Science is the study of the generalizable extraction of knowledge from data. Unprecedented advances in digital technology during the second half of the 20th century and the data explosion that ensued in the 21st century is transforming the way we do science, social science, and engineering. Application of data science cut across all verticals. The whole idea of this course is to introduce the students with the sole foundations/mathematics of data science. The course will cover topics such as [`Probability Distributions`](https://en.wikipedia.org/wiki/Probability_distribution), [`Mathematical Optimization`](https://en.wikipedia.org/wiki/Mathematical_optimization), [`Big Data`](https://en.wikipedia.org/wiki/Big_data), [`Machine Learning`](https://en.wikipedia.org/wiki/Machine_learning), etc. +📄 [Course Handout](https://drive.google.com/file/d/1v5zFeoNV1AwrCe29tXkfyg5Kf-YyUX-l/view?usp=sharing) ## Navigation diff --git a/courses/CSF407/README.md b/courses/CSF407/README.md index 7a2c6c2..1010213 100644 --- a/courses/CSF407/README.md +++ b/courses/CSF407/README.md @@ -4,6 +4,8 @@ AI introduces students to basic concepts and methods of Artificial Intelligence from a computer science perspective. AI concerns itself with a certain set of problems and develops a particular body of techniques for approaching these problems. This course will empower the students to know how to program computers, using classical symbolic methods, to behave in ways normally attributed to "Intelligence" when observed in humans. Main topics discussed in the course include : [`Search Techniques`](https://en.wikiversity.org/wiki/Search_techniques), [`Game Playing`](https://cs.anu.edu.au/courses/comp1110/lectures/pdf/Z01.pdf), [`Knowledge Representation`](https://www.javatpoint.com/knowledge-representation-in-ai), [`Reasoning`](https://www.javatpoint.com/reasoning-in-artificial-intelligence), [`Uncertainty`](https://www.javatpoint.com/probabilistic-reasoning-in-artifical-intelligence), [`Planning`](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling), [`Machine Learning`](https://en.wikipedia.org/wiki/Machine_learning) & [`Natural Language Processing`](https://en.wikipedia.org/wiki/Natural_language_processing). +📄 [Course Handout](https://drive.google.com/file/d/1-cqejKYtJzyHe5jmdRzl_ZocV4X7wWqY/view?usp=drive_link) + ## Navigation * [Prerequisites](#prerequisites) diff --git a/courses/CSF415/README.md b/courses/CSF415/README.md index 1e9cb16..98322c5 100644 --- a/courses/CSF415/README.md +++ b/courses/CSF415/README.md @@ -4,6 +4,8 @@ The course explores the concepts and techniques of Data Mining, a promising and flourishing frontier in database systems. Data Mining is automated extraction of patterns representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. The course covers data mining tasks like constructing [`Decision trees`](https://en.wikipedia.org/wiki/Decision_tree), finding [`Association rules`](https://en.wikipedia.org/wiki/Association_rule_learning), [`Classification`](https://en.wikipedia.org/wiki/Statistical_classification) and [`Clustering`](https://en.wikipedia.org/wiki/Cluster_analysis). The course is designed to provide students with a broad understanding in the design and use of data mining algorithms. The course also aims at providing a holistic view of data mining. +📄 [Course Handout](https://drive.google.com/file/d/1iDmCuwe9BvNfG_DXJUCkjd9tXj1vbLvA/view?usp=drive_link) + ## Navigation * [Prerequisites](#prerequisites) diff --git a/courses/CSF425/README.md b/courses/CSF425/README.md index 794e09d..61d3a6f 100644 --- a/courses/CSF425/README.md +++ b/courses/CSF425/README.md @@ -11,6 +11,8 @@ solutions for basic vision tasks such as [`image classification`](https://www.te models for such tasks. Students will learn to implement, train and debug their own neural networks. This is a project oriented practical course in which every student has to develop a complete working model to solve some real-world problem. +📄 [Course Handout](https://drive.google.com/file/d/17m5c7_kq8a79cHsQF07C52nGpkzCxpUT/view?usp=drive_link) + ## Navigation * [Prerequisites](#prerequisites) diff --git a/courses/CSF463/README.md b/courses/CSF463/README.md index eea6218..8c21236 100644 --- a/courses/CSF463/README.md +++ b/courses/CSF463/README.md @@ -4,6 +4,8 @@ Topics include Ancient ciphers, modern stream and block ciphers, `DES`, `AES`, `Public Key Encryption` & `Key Management`. The course also covers related mathematics in [`number theory`](https://brilliant.org/wiki/number-theory/) and [`group theory`](https://brilliant.org/wiki/group-theory-introduction/). +📄 [Course Handout](https://drive.google.com/file/d/1LyYBbb8-HcKSkxGgyZGyzowbNzLNU6h1/view?usp=drive_link) + ## Navigation * [Prerequisites](#prerequisites) diff --git a/courses/CSF464/README.md b/courses/CSF464/README.md index 2eae795..3ac5aff 100644 --- a/courses/CSF464/README.md +++ b/courses/CSF464/README.md @@ -3,6 +3,7 @@ ## Overview The course introduces the key algorithms and theory that forms the core of machine learning. It covers Major Approaches such as [supervised](https://en.wikipedia.org/wiki/Supervised_learning), [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning), [semi-supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning), and [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning). Topics covered include regression, decision trees, suport vector machines, artificial neural networks, Bayesian techniques, Hidden Markov, etc. +📄 [Course Handout](https://drive.google.com/file/d/1sxGSP_kGzFAr-ATYe-0Av_SNttRWETiE/view?usp=drive_link) ## Navigation