Tweak wording in Discrete Math, add Theory descriptions, add extra resource to Cybersecurity, link individual Machine Learning courses

Author: Flomza

docs/computer-science/applications/machine-learning/index.md

@@ -5,5 +5,10 @@ sidebar_position: 2
 # Machine Learning
 *Timeline: 99 hours*
 
-This course gives a basic introduction to Machine Learning:
-- [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction)
+These courses provide a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
+
+- [Supervised Machine Learning: Regression and Classification](https://www.coursera.org/learn/machine-learning), 33 hrs
+
+- [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms), 34 hrs
+
+- [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning), 27 hrs

docs/computer-science/ethics-security/cyber-fundamentals/index.md

@@ -6,4 +6,7 @@ sidebar_position: 2
 *Timeline: 80 - 96 hours*
 
 Learn cybersecurity fundamentals, including how to detect threats, protect systems and networks, and anticipate potential cyber attacks.
-- [Cybersecurity Fundamentals](https://www.edx.org/learn/cybersecurity/rochester-institute-of-technology-cybersecurity-fundamentals)
+- [Cybersecurity Fundamentals](https://www.edx.org/learn/cybersecurity/rochester-institute-of-technology-cybersecurity-fundamentals)
+
+Extra resources:
+- [Professor Messer SY0-701 Security+ Training Course](https://www.youtube.com/playlist?list=PLG49S3nxzAnl4QDVqK-hOnoqcSKEIDDuv)

docs/computer-science/math/discrete-math/index.md

@@ -9,7 +9,7 @@ Covers everything typically found in a Discrete Math course.
 - [Mathematics for Computer Science 2019](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+6.042J+2T2019/about), 65 hrs
 - Alternative: [Mathematics for Computer Science 2010](https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-fall-2010/)
 
-Many students choose to go with a paid textbook for easier introduction to this subject:
+Many students choose to go with a paid textbook for an easier introduction to this subject:
 - [Discrete Mathematics with Applications](https://www.amazon.com/Discrete-Mathematics-Applications-Susanna-Epp/dp/1337694193/), 1 year
 
 This book covers some topics which are covered again in [theory courses](/computer-science/theory/index.md). The MIT course and the corresponding book covers more topics, which are not covered by this book.

docs/computer-science/theory/index.md

@@ -6,7 +6,19 @@ sidebar_position: 7
 *Timeline: 64 - 128 hours*
 
 Covers everything typically found on the theoretical side of a Data Structures & Algorithms course. Introduction to DSA was covered throughout [Program Design](../program-design/).
+
+The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
+
 - [Algorithms 1](https://www.coursera.org/learn/algorithms-divide-conquer), 16-32 hrs
+
+The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
+
 - [Algorithms 2](https://www.coursera.org/learn/algorithms-graphs-data-structures), 16-32 hrs
+
+The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
+
 - [Algorithms 3](https://www.coursera.org/learn/algorithms-greedy), 16-32 hrs
+
+The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).
+
 - [Algorithms 4](https://www.coursera.org/learn/algorithms-npcomplete), 16-32 hrs